Yonghui Xu

Yonghui Xu
Shandong University | SDU · Joint SDU-NTU Centre for Artificial Intelligence Research

Professor

About

104
Publications
13,220
Reads
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923
Citations

Publications

Publications (104)
Article
Full-text available
The effectiveness of deep learning‐based methods for insulator defect detection has been proven. However, in practical applications of power transmission lines, the complex and variable backgrounds in insulator images, coupled with the difficulty in labeling insulator defects, pose challenges to improving the robustness of such methods. Existing st...
Article
Federated domain adaptation (FDA) aims to transfer knowledge collaboratively from multiple source domains to related but different unlabelled target domains. The data of each domain are locally maintained, and various domain gaps exist among them, resulting in extreme challenges in simultaneously mitigating diverse distribution shifts and preservin...
Article
As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and governance of complex adaptive systems. However, existing modeling methods struggle to accurately repr...
Conference Paper
Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learn...
Preprint
Full-text available
Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate with their unique dietary predilections. Typically, multi-modal descriptions offer an exhaustive profile for each recipe, thereby ensuring recommendations that are both pers...
Article
Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. P...
Article
Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a munderlineti-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However,...
Article
Electronic health record (EHR) data is crucial in providing comprehensive historical disease information for patients and is frequently utilized in health event prediction. However, current deep learning models that rely on EHR data encounter significant challenges. These include inadequate exploration of higher‐order relationships among diseases,...
Article
Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) ca...
Article
Accurately estimating packages’ arrival time in e-commerce can enhance users’ shopping experience and improve the placement rate of products. This problem is often formalized as an Origin-Destination (OD)-based ETA ( i.e. estimated time of arrival) prediction task, where the delivery time is estimated mainly based on sender and receiver addresses a...
Chapter
There are many ways to process graph data in deep learning, among which Graph Neural Network(GNN) is an effective and popular deep learning model. However, GNN also has some problems. For example, after multiple layers of neural networks, the features between nodes will become more and more similar, so that the model identifies two completely diffe...
Chapter
Distributed graph representation learning refers to the process of learning graph data representation in a distributed computing environment. In the process of distributed graph representation learning, nodes need to exchange data frequently, making data transmission crucial in this context. The content of data transmission, including plaintext dat...
Article
Federated heterogeneous graph learning helps users mine distributed heterogeneous data from consumer electronics while protecting data privacy. However, traditional federated graph learning methods often concentrate solely on aggregating model parameters, overlooking the global structure inherent in intricate heterogeneous graphs from various consu...
Article
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Patient representation learning aims to encode meaningful information about the patient’s Electronic Health Records (EHR) in the form of a mathematical representation. Recent advances in deep learning have empowered Patient representation learning methods with greater representational power, allowing the learned representations to significantly imp...
Article
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Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods...
Article
Digital music has various characteristics, such as melody, rhythm, timbre, and harmony. According to these characteristics, music can be classified using artificial intelligence (AI). Music can reduce cognitive dissonance and improve memory in humans; however, occasionally, dissonant music can cause negative effects, such as aggravating depression....
Article
The evaluation and prediction of credit risk have always been a research hotspot to ensure the healthy and orderly development of the credit market. Most researchers use deep learning to predict credit risk. However, when training data are too small, deep learning models often lead to overfitting. Although we have a large amount of available traini...
Preprint
Full-text available
Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enabling unsupervised representation learning is extremely crucial for time series data, due to its unique annotation bottleneck caused by its complex characteristics and lack o...
Article
Full-text available
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance inde...
Article
Full-text available
Automatic medical report generation is an essential task in applying artificial intelligence to the medical domain, which can lighten the workloads of doctors and promote clinical automation. The state-of-the-art approaches employ Transformer-based encoder-decoder architectures to generate reports for medical images. However, they do not fully expl...
Preprint
Full-text available
Insulators are essential and numerous components in power transmission lines, but they are also prone to faults. Therefore, it is crucial to detect faults in insulators. Although existing fault detection methods for insulators in power transmission lines have been improved to some extent by continuously modifying their internal structures, traditio...
Article
Full-text available
Compared with cloud computing, edge–cloud collaboration can avoid long transmitting delay to cloud since tasks are close to edge, which makes edge–cloud collaboration suitable for delay-sensitive applications. However, the complex environment of edge–cloud poses new challenge to task scheduling. A Collaborative Scheduling strategy based on task Adm...
Article
Full-text available
Domain adaptation (DA) aims to transfer knowledge from one source domain to another different but related target domain. The mainstream approach embeds adversarial learning into deep neural networks (DNNs) to either learn domain-invariant features to reduce the domain discrepancy or generate data to fill in the domain gap. However, these adversaria...
Chapter
Full-text available
Federated Learning (FL), a novel distributed machine learning framework, made it possible to model collaboratively without risking participants’ privacy. All components of FL, including devices, networks, data, and models, are heterogeneous because of the dispersed feature. These heterogeneity issues impeded FL’s performance. HFL (Heterogeneous Fed...
Article
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With the accumulation of check-in data from location-based services, next Point-of-Interest (POI) recommendations are gaining increasing attention. It is well known that the spatio-temporal contextual information of user check-in behavior plays a crucial role in handling vital and inherent challenges in next POI recommendation, including capture of...
Preprint
Full-text available
Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client. Moreover, the source clients have different data distributions, leading to extremely challenging in knowledge transfer. Despite the recent progress...
Chapter
Full-text available
Automatic medical image report generation has attracted extensive research interest in medical data mining, which effectively alleviates doctors’ workload and improves report standardization. The mainstream approaches adopt the Transformer-based Encoder-Decoder architecture to align the visual and linguistic features. However, they rarely consider...
Chapter
Full-text available
Knowledge graph completion is to infer missing/new entities or relations in knowledge graphs. The long-tail distribution of relations leads to the few-shot knowledge graph completion problem. Existing solutions do not thoroughly solve this problem, with the few training samples still deteriorating knowledge graph completion performance. In this pap...
Chapter
Full-text available
Disease prediction as an important component of medical assistant diagnostic systems has received much attention from researchers. Many studies attempt to extract disease-related features from electronic health records (EHRs). However, the heterogeneity of entities (e.g., diseases, symptoms, medications, and other treatment items) in EHRs is usuall...
Chapter
Full-text available
Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address, and payment time. Accurate DTE can boost platform revenue and reduce customer complaints and refunds. However, the imbalanced nature of industrial data impedes previous m...
Preprint
Full-text available
Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address, and payment time. Accurate DTE can boost platform revenue and reduce customer complaints and refunds. However, the imbalanced nature of industrial data impedes previous m...
Chapter
Full-text available
Clinical Phenotyping is a fundamental task in clinical services, which assessments whether a patient suffers a medical condition of interest. Existing works focus on learning better patients’ representations. Recently, multi-task learning has been proposed to transfer knowledge from different tasks and achieved promising performance. However, the e...
Preprint
Full-text available
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up m...
Preprint
Full-text available
Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to...
Preprint
Full-text available
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance inde...
Chapter
Full-text available
Mobile crowdsourcing (MC) which has been developed rapidly in recent years is playing an increasingly indispensable role in people’s daily lives such as taxi-hailing, food delivery and other services. The geographic equilibrium of service supply and demand is crucial so that the MC system could guarantee more promising matches in a more regionally...
Article
Full-text available
Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients’ health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predictive analysis of patient outcomes. However, most existing works on this task suffer from the followin...
Conference Paper
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The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to l...
Article
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Knowledge graph embedding, which aims at learning the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware grap...
Preprint
Full-text available
The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to l...
Article
Full-text available
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to a related but unlabeled target domain. Most existing approaches either adversarially reduce the domain shift or use pseudo-labels to provide category information during adaptation. However, an adversarial training method may sacrifice the discriminability of t...
Chapter
Full-text available
Writing medical image reports is an inefficient and time-consuming task for doctors. Automatically generating medical reports is an essential task of medical data mining, which can alleviate the workload of doctors and improve the standardization of reports. However, the existing methods mainly adopt the CNN-RNN structure to align image features wi...
Chapter
Full-text available
The rapid growth in the use of electronic health records (EHR) offers promises for predicting patient outcomes. Previous works on this task focus on exploiting temporal patterns from sequential EHR data. Nevertheless, such approaches model patients independently, missing out on the similarities between patients, which are crucial for patients’ heal...
Chapter
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Explainable recommendation systems not only provide users with recommended results but also explain why they are recommended. Most existing explainable recommendation methods leverage sentiment analysis to help users understand reasons for recommendation results. They either convert particular preferences into sentiment scores or simply introduce t...
Chapter
Full-text available
The affordance theory provides a biology-inspired approach to enable a robot to act, think and develop like human beings. Based on existing affordance relationships, a robot understands its environment and task in terms of potential actions that it can execute. Deep learning makes it possible for a robot to perceive the environment in an efficient...
Article
Full-text available
In recent years, neural networks have been widely used in natural language processing, especially in sentence similarity modeling. Most of the previous studies focused on the current sentence, ignoring the commonsense knowledge related to the current sentence in the task of sentence similarity modeling. Commonsense knowledge can be remarkably usefu...
Article
The target of product attributes prediction is to complete the characteristics set for defining a particular product. Most of the existing methods treat the product attributes prediction as a Named-Entity Recognition (NER) problem from the products’ affiliated data, such as product title and introduction. However, in a large number of industrial ap...
Article
Vertical Federated Learning (VFL) is a private-preserving distributed machine learning paradigm that collaboratively trains machine learning models with participants whose local data overlap largely in the sample space, but not so in the feature space. Existing VFL methods are mainly based on synchronous computation and homomorphic encryption (HE)....
Preprint
Full-text available
On the increase of major depressive disorders (MDD), many researchers paid attention to their recognition and treatment. Existing MDD recognition algorithms always use a single time-frequency domain method method, but the single time-frequency domain method is too simple and is not conducive to simulating the complex link relationship between brain...