Dapeng Oliver Wu's research while affiliated with The University of Hong Kong and other places
What is this page?
This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
Publications (322)
Abundant real-time applications over Internet of things (IoT) have imperative demands on timely information. Compared to average age of information (AoI), distribution of AoI characterizes the timeliness in more details. This paper studies the timeliness of an IoT-based multi-source status update system. By modeling the system as a multi-source M/G...
The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in a large-scale power outage, leading to significant economic losses. To assess the impact of grid f...
Reconfigurable intelligent surface (RIS) is an emerging technology for enabling the manipulation of wireless environments, which shows great potential for the coverage and capacity enhancements of wireless networks. In this letter, a hybrid deployment scheme of terrestrial and aerial RISs (TRIS, ARIS) is proposed to avoid the obstruction of light-o...
This paper is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizin...
This paper addresses the problem of cross-modal object tracking from RGB videos and event data. Rather than constructing a complex cross-modal fusion network, we explore the great potential of a pre-trained vision Transformer (ViT). Particularly, we delicately investigate plug-and-play training augmentations that encourage the ViT to bridge the vas...
Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images...
In this paper, we propose a single-agent Monte Carlo-based reinforced feature selection method, as well as two efficiency improvement strategies, i.e., early stopping strategy and reward-level interactive strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature subset for a give...
Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communication...
Topology robustness is critical to the connectivity and lifetime of large-scale Internet-of-Things (IoT) applications. To improve robustness while reducing the execution cost, the existing robustness optimization methods utilize neural learning schemes, including neural networks, deep learning, and reinforcement learning. However, insufficient expl...
This paper studies the joint three-dimensional (3D) deployment and beamforming problem for a rate-splitting multiple access (RSMA)-enabled unmanned aerial vehicle base station (UBS) assisted by geographic information. Specifically, we maximize the minimum achievable rate among users by optimizing the beamforming, rate allocation and UBS deployment...
Internet of Things (IoT) as a ubiquitous networking paradigm has been experiencing serious security and privacy challenges with the increasing data in diversified applications. Fortunately, this will be, to a great extent, alleviated with the emerging blockchain, which is a decentralized digital ledger based on cryptography and has offered potentia...
Network Functions Virtualization (NFV), which decouples network functions from the underlying hardware, has been regarded as an emerging paradigm to provide flexible virtual resources for various applications through the ordered interconnection of Virtual Network Functions (VNFs), in the form of Service Function Chains (SFCs). In order to achieve t...
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Interne...
Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optim...
Unmanned aerial vehicles (UAVs) have emerged as a promising solution for enhancing mobile edge computing (MEC) networks. However, the integration of UAVs into MEC networks poses unique challenges, such as the presence of dynamic devices and complex resource allocation. This research investigates the problem of task offloading in a distributed MEC n...
For moving cameras, the video content changes significantly, which leads to inaccurate prediction in traditional inter prediction and results in limited compression efficiency. To solve these problems, first, we propose a camera pose-based background modeling (CP-BM) framework that uses the camera motion and the textures of reconstructed frames to...
Neural network pruning has been a well-established compression technique to enable deep learning models on resource-constrained devices. The pruned model is usually specialized to meet specific hardware platforms and training tasks (defined as deployment scenarios). However, existing pruning approaches rely heavily on training data to trade off mod...
The span of Internet of Things (IoT) is expanding owing to numerous applications being linked to massive devices. Subsequently, node failures frequently occur because of malicious attacks, battery exhaustion, or other malfunctions. A reliable and robust network topology can alleviate the cascading collapse caused by local node failures. Existing op...
The accurate prediction of protein-ligand binding affinity is critical for the success of computer-aided drug discovery. However, the accuracy of current scoring functions is usually unsatisfactory due to their rough approximation or sometimes even omittance of many factors involved in protein-ligand binding. For instance, the intrinsic dynamics of...
A cellular unmanned-aerial-vehicle (UAV)-enabled system is studied in this research, where multiple UAVs work cooperatively to upload the collected sensory data to the base station (BS). The UAVs form a two-tier network with two kinds of communication modes, i.e., UAV-to-Network (U2N) and UAV-to-UAV (UAV) communications. We investigate the communic...
Lots of real-time applications over Internet of things (IoT)-based status update systems have imperative demands on information freshness, which is usually evaluated by age of information (AoI). Compared to the average AoI and peak AoI (PAoI), violation probabilities and distributions of AoI and PAoI characterize the timeliness in more details. Thi...
This paper presents GeoDMA , which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal de...
Federated learning enables multiple distributed devices to collaboratively learn a shared prediction model without centralizing their on-device data. Most of the current algorithms require comparable individual efforts for local training with the same structure and size of on-device models, which, however, impedes participation from resource-constr...
This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication. We collected ecologically-valid computer usage profi...
In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Several approaches exist that utilize the evolutionary information from pathogen genomic data derived from infected individuals to distinguish these groups from the backg...
Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies show that machine learning services face severe privacy threats - well-trained DNNs owned by MLaaS providers can be stolen through public APIs, namely model stealing att...
Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source domain to understand a similar yet distinct unlabeled target domain. Notably, global distribution statistics across domains and local semantic characteristics across samples, are two essential factors of data analysis that should be fully explored. Most existing...
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and communication tasks quickly, robustly, safely, and cost-efficiently via effective situation awareness and cooperation among UAVs. To achieve the promising benefits, the crucial IUAV networking issue should be tackled. This article argues that I-UAV networking can...
The emerging network-softwarization technologies such as Software Defined Networking and Network Function Virtualization play important roles in 5G communication and future networks. One of the critical challenges of the practical application of the softwarized networks is to appropriately place virtual network functions (VNFs). The underlying reso...
Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject...
Unmanned aerial vehicles (UAVs) play a crucial role in emergency-oriented applications. However, in UAV-aided Internet of Things (IoT) networks, the sensor nodes (SNs) would be mobile which poses a big challenge for trajectory planning of the UAV. In this paper, we investigate priority-oriented UAV-aided time-sensitive data collection problems in a...
Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the re...
Federated Edge Learning considers a large amount of distributed edge nodes collectively train a global gradient-based model for edge computing in the Artificial Internet of Things, which significantly promotes the development of cloud computing. However, current federated learning algorithms take tens of communication rounds transmitting unwieldy m...
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs. To achieve the promising benefits, the crucial I-UAV networking issue should be tackled. This article argues that I-UAV networking can be classified into three catego...
With the wide deployment of edge devices, a variety of emerging applications have been deployed at the edge of network. To guarantee the safe and efficient operations of the edge applications, especially the extensive web applications, it is important and challenging to detect packet payload anomalies, which can be expressed as a number of specific...
The accurate prediction of protein-ligand binding affinity is critical for the success of computer-aided drug discovery. However, the accuracy of current scoring functions is usually unsatisfactory due to their rough approximation or sometimes even omittance of many factors involved in protein-ligand binding. For instance, the intrinsic dynamic of...
Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes, such as spectral clustering, cannot be applied to a large network due to prohibitive computational complexity required. While th...
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES) strategy and reward-level interactive (RI) strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature...
Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology to increase resistance against malicious attacks is a complex problem, especially on 3-dimension (3D) topological deployment. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such probl...
In the areas after natural disaster strikes, the ground communication network can be failed, due to the damage of the communication infrastructure. However, during or after the natural disasters such as earthquakes or tsunamis, ground vehicles may not enter the effected areas easily to set up mobile base stations. Unmanned Aerial Vehicles (UAVs) ca...
Widespread deployment of the Internet of Things (IoT) has changed network services in which people, processes, data, and things connect each other. According to Cisco, 500 billion devices are expected to be connected to the Internet by 2030, where IoT is the network of these connected devices. Currently, most advanced IoT devices are equipped with...
![Recall Score comparison with Message Passing [10] on Visual Genome dateset.](publication/354182683/figure/tbl1/AS:1061772538675200@1630157708339/Recall-Score-comparison-with-Message-Passing-10-on-Visual-Genome-dateset_Q320.jpg)
![Recall Score comparison with aGCN [11] on Visual Genome dateset.](publication/354182683/figure/tbl2/AS:1061772538687490@1630157708370/Recall-Score-comparison-with-aGCN-11-on-Visual-Genome-dateset_Q320.jpg)
Lots of machine learning tasks require dealing with graph data, and among them, scene graph generation is a challenging one that calls for graph neural networks’ potential ability. In this paper, we present a definition of graph neural network (GNN) consists of node, edge and global attribute, as well as their corresponding update and aggregate fun...
The statistical values of the latencies between two sets of hosts over a given period, which is referred as to statistical latency, can benefit many applications in the next-generation networks, such as Network in a Box (NIB) based resource provisioning. However, existing methods can hardly achieve low measurement cost and high prediction accuracy...
This paper is concerned with the security problem of Time of Arrival (ToA) based localization schemes in a wireless sensor network (WSN) with multiple attackers and this paper focuses on defending against external attacks, especially under cooperative external attackers. The prior scheme for defending against the attacks in the localization scheme...
Indoor positioning technology based on Wi-Fi fingerprint recognition has been widely studied owing to the pervasiveness of hardware facilities and the ease of implementation of software technology. However, the similarity-based method is not sufficiently accurate, whereas the offline training of the neural network-based method is overly time-consum...
In this paper, we develop a novel global-attention-based neural network (GANN) for vision language intelligence, specifically, image captioning (language description of a given image). As many previous works, the encoder-decoder framework is adopted in our proposed model, in which the encoder is responsible for encoding the region proposal features...
The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications th...
Federated learning (FL) enables multiple clients to collaboratively build a global learning model without sharing their own raw data for privacy protection. Unfortunately, recent research still found privacy leakage in FL, especially on image classification tasks, such as the reconstruction of class representatives. Nevertheless, such analysis on i...
This paper investigates whether computer usage profiles comprised of process-, network-, mouse- and keystroke-related events are unique and temporally consistent in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication. We collected ecologically-valid computer usage profi...
Recognizing human expression in videos is a challenging task due to dynamic changes in facial actions and diverse visual appearances. The key to design a reliable video-based expression recognition system is to extract robust spatial features and make full use of temporal modality characteristics. In this paper, we present a novel network architect...
Recently increasing real-time network applications have raised a new requirement for the future 6G network transmission, which desires the information-update packets arrive at the receiver as timely as possible. Multipath TCP (MPTCP) can be employed in the real-time update transmission system by making use of multiple paths so as to promote the inf...
With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being...
Future wireless networks are envisioned to serve massive Internet of things (mIoT) via some radio access technologies, where the random access channel (RACH) procedure should be exploited for IoT devices to access the networks. However, the theoretical analysis of the RACH procedure for massive IoT devices is challenging. To address this challenge,...
Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server. The global model is optimized by training and averaging the model parameters of all local participants. However, the improved privacy of federated learning also introduces challenges including higher com...
To enable insight extractions without the risk of information leakage, deep learning (DL) models are increasingly built on federated edge participants holding local data, in combination with differential privacy (DP). The core theme is to tradeoff learning accuracy by adding statistically calibrated noises, particularly to local gradients of edge l...
Mobile Opportunistic Networks (MONs) are characterized by the lack of continuous end-to-end connectivity between two nodes due to node mobility, sparse deployment, and constrained resources. In order to fulfill ubiquitous communication requirements of them, social-aware routing, which exploits social ties and behaviors among nodes to make forwardin...
Computed tomography (CT) images are formally taken as an assistance of early diagnosis in lung nodule analysis. Thus the accurate lung nodule segmentation is in great need for image-driven tasks. However, as heterogeneity exists between different types of lung nodules, the similar visual appearance between the pixels of nodules and pixels of non-no...
Occupying the most significant portion of global data traffic, video is being generated in almost every aspect of our life. Because of its huge volume, we are depending much more heavily on machine intelligence based analysis. In the meantime, video coding technology has been continuously improved for better compression efficiency. However, the sta...
Estimating the depth of image and egomotion of agent are important for autonomous and robot in understanding the surrounding environment and avoiding collision. Most existing unsupervised methods estimate depth and camera egomotion by minimizing photometric error between adjacent frames. However, the photometric consistency sometimes does not meet...
Graph Neural Networks (GNNs) have been applied in many fields of semi-supervised node classification for non-Euclidean data. However, some GNNs cannot make good use of positive information brought by nodes which are far away from each central node for aggregation operations. These remote nodes with positive information can enhance the representatio...
Large volumes of video data recorded by the increasing mobile devices and embedded sensors can be leveraged to answer queries of our lives, physical world and our evolving society. Especially, the rapid development of convolutional neural networks (CNNs) in the past few years offers the great advantage for multiple tasks in video analysis. However,...
Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive dis...
This paper is concerned with slicing a radio access network (RAN) for simultaneously serving two 5G-and-Beyond typical use cases, i.e., enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC). Although many researches have been conducted to tackle this issue, few of them have considered the impact of bursty URLLC....
Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In...
Huanglongbing (HLB) is a devastating citrus disease worldwide. A three-pronged approach to controlling HLB has been suggested, namely, removal of HLB-symptomatic trees, psyllid control, and replacement with HLB-free trees. However, such a strategy did not lead to successful HLB control in many citrus producing regions. We hypothesize this is becaus...
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction w...
Recently web applications have been widely used in enterprises to assist employees in providing effective and efficient business processes. Forecasting upcoming web events in enterprise web applications can be beneficial in many ways, such as efficient caching and recommendation. In this paper, we present a web event forecasting approach, DeepEvent...
Random mobility models (RMMs) capture the random mobility patterns of mobile agents, and have been widely used as the modeling framework for the evaluation and design of mobile networks. All existing RMMs in the literature assume independent movements of mobile agents, which does not hold for unmanned aircraft systems (UASs). In particular, UASs mu...
Unmanned aerial vehicle (UAV) relay networks are convinced to be a significant complement to terrestrial infrastructures to provide robust network capacity. However, most of the existing works either considered enhanced mobile broadband (eMBB) payload communication or ultra-reliable and low latency communications (URLLC) control information communi...
Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To...
Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompletene...
Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results....
Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low code...
Erasure coding is widely used in distributed storage systems (DSSs) to efficiently achieve fault tolerance. However, when the original data need to be updated, erasure coding must update every encoded block, resulting in long update time and high bandwidth consumption. Exiting solutions are mainly focused on coding schemes to minimize the size of t...
In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs,...
Internet of Underwater Things (IoUT) has a wide range of application prospects in civil and military fields. As a key enabling technology of IoUT, Underwater acoustic sensor networks (UASNs) feature a variety of unique characteristics, including high latency, high mobility and low bandwidth. All these problems pose challenges in the design of effic...
Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies show that machine learning services face severe privacy threats - well-trained DNNs owned by MLaaS providers can be stolen through public APIs, namely model stealing att...
Current federated learning algorithms take tens of communication rounds transmitting unwieldy model weights under ideal circumstances and hundreds when data is poorly distributed. Inspired by recent work on dataset distillation and distributed one-shot learning, we propose Distilled One-Shot Federated Learning, which reduces the number of communica...