Dingqi Yang

• PhD
• Associate Professor at University of Macau

Capturing place semantics is critical for enabling location-based applications. Techniques for assigning semantic labels (e.g., " bar " or " office ") to un-labeled places mainly resort to mining user activity logs by exploiting visiting patterns. However, existing approaches focus on inferring place labels with a static user activity dataset, and...

Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usua...

With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users' spatial temporal activity preference (STAP), which can enable a wide range...

Crowdsourcing platforms for disaster management have drawn a lot of attention in recent years due to their efficiency in disaster relief tasks, especially for disaster data collection and analysis. Although the on-site rescue staff can largely benefit from these crowdsourcing data, due to the rapidly evolving situation at the disaster site, they us...

The crowdsourced digital footprints from Location Based Social Networks (LBSNs) contain not only rich information about locations, but also individual's feeling about locations and associated entities. This new data source provides us with an unprecedented opportunity to massively and cheaply collect location related information, and to subtly char...

The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inher...

As environmental awareness increased due to the surge in greenhouse gases, green travel modes such as bicycles and walking have gradually became popular choices. However, the current traffic environment has many hidden problems that endanger the personal safety of traffic participants and hinder the development of green travel. Traditional methods,...

LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from their individual strengths -- has gained substantial attention recently. The widespread availability of LLMs, coupled with their varying strengths and out-of-the-box usabilit...

Next-location prediction aims to forecast which location a user is most likely to visit given the user's historical data. As a sequence modeling problem by nature, it has been widely addressed using Recurrent Neural Networks (RNNs). To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as...

Understanding crowd mobility is critical for many applications. In this paper, we propose CrowdMirage, a WiFi positioning-based crowd mobility digital twin for smart campuses. Specifically, we first design an end-to-end human mobility trace extraction pipeline from the comprehensive but noisy WiFi connection logs on a university campus. We then des...

The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inher...

Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, such as the retweets of a tweet...

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary s...

During the past few years, with the advent of large-scale pre-trained language models (PLMs), there has been a significant advancement in cross-domain text classification with limited labeled samples. However, most existing approaches still face the problem of excessive computation overhead. While some non-pretrained language models can reduce the...

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these...

Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To add...

As a fundamental problem in human mobility modeling, location prediction forecasts a user’s next location based on historical user mobility trajectories. Recurrent Neural Networks (RNNs) have been widely used to capture sequential patterns of user visited locations for solving location prediction problems. Due to the sparse nature of real-world use...

Understanding the vulnerability of label aggregation against data poisoning attacks is key to ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms generally assume full knowledge of the aggregation models while failing to consider the flexibility of malicious workers in selecting which instances to label. Such...

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects - most comm...

Mobile Crowdsourcing (MCS), a human-centric promising paradigm for performing location-based tasks, has drawn rising attention from both academia and industry. In MCS applications, the outsourced tasks are allocated by a management platform to a group of recruited workers. However, during real-world task implementation, various types of unpredictab...

Modern Knowledge Graphs (KG) often suffer from an incompleteness issue (i.e., missing facts). By representing a fact as a triplet $(h,r,t)$ linking two entities $h$ and $t$ via a relation $r$ , existing KG completion approaches mostly consider a link prediction task to solve this problem, i.e., given two elements of a triplet predicting the...

Mobile Crowdsensing (MCS) is a sensing paradigm that enables large-scale smart city applications, such as environmental sensing and traffic monitoring. However, traditional MCS often suffers from performance degradation due to the limited spatiotemporal coverage of collected data. In this context, Sparse MCS has been proposed, which utilizes data i...

Knowledge Graph (KG) embeddings have become a powerful paradigm to resolve link prediction tasks for KG completion. The widely adopted triple-based representation, where each triplet $(h,r,t)$ links two entities $h$ and $t$ through a relation $r$ , oversimplifies the complex nature of the data stored in a KG, in particular for hyper-relatio...

Crowd flow prediction is one of the key problems in human mobility modeling, forecasting crowd flows of locations based on historical human mobility traces. Traditional human mobility traces (collected via telecommunication companies, online social media platforms, or field studies/experiments, etc.) suffer from severe data quality issues such as l...

Until recently, a novel spatial crowdsourcing paradigm, namely Three-Dimensional (3D) spatial crowdsourcing, has emerged, in which the task requestors and the workers need travel to their designated third-party workplaces, e.g., shared offices, to deliver certain services, such as DiDi station ride-sharing service, Quyundong sport training service...

Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algor...

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks. Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph structures e...

Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the b...

Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by the low...

Graph embeddings have been widely used for many graph analysis tasks. Mainstream factorization-based and graph-sampling-based embedding learning schemes both involve many hyperparameters and design choices. However, existing techniques often adopt some heuristics for these hyperparameters and design choices with little investigation into their impa...

In this paper, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such th...

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable...

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable...

The surging data traffic and dynamic user mobility in 5G networks have posed significant demands for mobile operators to increase data processing capacity and ensure user handover quality. Specifically, a cost-effective and quality-aware radio access network (RAN) is in great necessity. With the emergence of fog-computing-based RAN architecture (Fo...

With the increasing prominence of smart mobile devices, an innovative distributed computing paradigm, namely Mobile Crowdsourcing (MCS), has emerged. By directly recruiting skilled workers, MCS exploits the power of the crowd to complete location-dependent tasks. Currently, based on online social networks, a new and complementary worker recruitment...

With the proliferation of increasingly powerful mobile devices and wireless networks, mobile crowdsourcing has emerged as a novel service paradigm. It enables crowd workers to take over outsourced location-dependent tasks, and has attracted much attention from both research communities and industries. In this paper, we consider a mobile crowdsourci...

With the proliferation of increasingly powerful smartphones, location-centric social media platforms, such as Foursquare, have attracted millions of users sharing their physical activity online, resulting in an invaluable source of fine-grained, semantically rich, spatiotemporal user activity data. Such data provides us with an unprecedented opport...

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by...

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by...

Location-Based Social Networks (LBSNs) have been widely used as a primary data source for studying the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users’ mobility homophily and social proximity, and show that mobility and social features can help friendship and location...

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and infer urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we propose a novel location obfuscation mechanism combining $\epsilon $ -differential-privacy and $\de...

The surging traffic volumes and dynamic user mobility patterns pose great challenges for cellular network operators to reduce operational costs and ensure service quality. Cloud Radio Access Network (C-RAN) aims to address these issues by handling traffic and mobility in a centralized manner, separating baseband units (BBUs) from base stations (RRH...

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expensive matrix factorization tech...

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order node proximity/adjacency matrix via computationally intensive matrix factorization techniques....

The presentation of the DAOR framework that performs graph embedding for arbitrary networks (i.e., graphs) without any manual tuning in near-linear time on the number of graph links producing interpretable embeddings robust in various metric spaces and preserving both low- and high-order proximities of the graph nodes.

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix...

This paper presents Scalpel-CD, a first-of-its-kind system that leverages both human and machine intelligence to debug noisy labels from the training data of machine learning systems. Our system identifies potentially wrong labels using a deep probabilistic model, which is able to infer the latent class of a high-dimensional data instance by exploi...

Location Based Social Networks (LBSNs) have been widely used as a primary data source to study the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users' mobility homophily and social proximity, and show that mobility and social features can help friendship and location pred...

In mobile crowdsourcing, organizers usually need participants’ precise locations for optimal task allocation, e.g., minimizing selected workers’ travel distance to task locations. However, the exposure of users’ locations raises privacy concerns. In this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation...

As an emerging social dynamic system, geo-social network can be used to facilitate viral marketing through the wide spread of targeted advertising. However, unlike traditional influence spread problem, the heterogeneous spatial distribution has to incorporated into geo-social network environment. Moreover, from the perspective of business managers,...

With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic d...

Different from online promotion, the outdoor billboard advertising industry suffers from a lack of audience-targeted delivery and quantitative dissemination evaluation, which undermine its impact in practice and hinder it from fast development. To bridge this gap, in the paper, we leverage crowdsensing vehicle trajectory data to empower audience-ta...

The graph embedding paradigm projects nodes of a graph into a vector space, which can facilitate various downstream graph analysis tasks such as node classification and clustering. To efficiently learn node embeddings from a graph, graph embedding techniques usually preserve the proximity between node pairs sampled from the graph using random walks...

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction...

Histogram-based similarity has been widely adopted in many machine learning tasks. However, measuring histogram similarity is a challenging task for streaming histograms, where the elements of a histogram are observed one after the other in an online manner. The ever-growing cardinality of histogram elements over the data streams makes any similari...

The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared am...

Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users' online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data...

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In t...

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In t...

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed dat...

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privac...

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context...

With the widespread adoption of smartphones, we have observed an increasing popularity of Location-Based Services (LBSs) in the past decade. To improve user experience, LBSs often provide personalized recommendations to users by mining their activity (i.e., check-in) data from location-based social networks. However, releasing user check-in data ma...

Understanding the irregular crowd movement and social activities caused by urban events such as city festivals and concerts can benefit event management and city planning. Although various urban data can be exploited to detect such irregularities, the crowd mobility data (e.g., bike trip records) are usually in a mixed state with several basic patt...

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing applications. In this paper, we leverage the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated (corresponding to budget), yet ensuring the data quality. Specifically,...

Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors for maximizing the utility of such systems is placing bike stations at locations that can best meet users' trip demand. Traditionally, urban planners rely on dedicated surveys to understand the local bike trip deman...

Understanding social activities in Urban Activity Centers can benefit both urban authorities and citizens. Traditionally, monitoring large social activities usually incurs significant costs of human labor and time. Fortunately, with the recent booming of urban open data, a wide variety of human digital footprints have become openly accessible, prov...

Offline event marketing invites people to participate in a sponsored gathering, thus allowing marketers to have face-to-face, direct, and close contact with their current and potential customers. This paper presents a framework that supports marketers in improving marketing effectiveness by carefully selecting invitees to such sponsored offline eve...

The research of collective behavior has attracted a lot of attention in recent years, which can empower various applications, such as recommendation systems and intelligent transportation systems. However, in traditional social science, it is practically difficult to collect large-scale user behavior data. Fortunately, with the ubiquity of smartpho...

We present WebVisor, an automated tool to derive patterns from malware Command and Control (C&C) server connections. From collective network communications stored on a large-scale malware dataset, WebVisor establishes the underlying patterns among samples of the same malware families (e.g., families in terms of development tools). WebVisor focuses...

Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behavio...

With the recent popularity of social network services, a significant volume of heterogeneous social media data is generated by users, in the form of texts, photos, videos and collections of points of interest, etc. Such social media data provides users with rich resources for exploring content, such as looking for an interesting video or a favorite...

With the recent surge of location-based social networks (LBSNs), such as Foursquare and Facebook Places, huge digital footprints of people's locations, profiles, and online social connections become accessible to service providers. Unlike social networks (e.g., Flickr, Facebook) that have explicit groups for users to subscribe to or join, LBSNs usu...

With the recent surge of location-based social networks (LBSNs), e.g., Foursquare and Facebook Places, huge amount of human digital footprints that people leave in the cyber-physical space become accessible, including users’ profiles, online social connections, and especially the places that they have checked in. Different from social networks (e.g...

Human memory often fails. People are frequently beset with questions like “Who is that person? I think I met him in Tokyo last year.” Existing memory aid tools cannot well support the recall of names effectively. This paper explores the memory recall enhancement issue from the perspective of memory cue extraction and associative search, and propose...

Ranking areas by popularity of a business category is an essential problem for business planning. Traditional approaches rely on economic and demographic factors nearby. However, the acquisition of relevant data is usually expensive. In this paper we propose a novel approach to address this problem by exploiting user-generated contents from locatio...

Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe us...

Crowd sourcing is a new paradigm of service provision. Current commercial crowd sourcing platforms rarely consider the interaction between task takers, which is extremely required in the disaster management scenario. In this paper, we designed a framework for community based crowd sourcing, i.e., task takers are from an existing community or will e...

With the recent surge of location-based social networks (LBSNs, e.g., Foursquare, Facebook Places), huge amount of digital footprints about users’ locations, profiles as well as their online social connections become accessible to service providers. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subs...

While the detection of social subgroups (i.e., communities) has always been a fundamental task in social network analysis, few efforts has been made to characterize the detected community. Meanwhile, to effectively facilitate applications based on the community structure, it is very important to understand the features of each community. Thereby, a...

Human memory is generally poor and often fails in unpredictable ways, sometimes with dire consequences. On social occasions, it usually causes embarrassing situations (e.g., forgetting the name of a friend). Moreover, as the number of contacts increases, people feel difficult to maintain their social contacts with merely memory. Aiming at helping p...