Jiannong Cao

• The Hong Kong Polytechnic University

With the rapid development of sixth-generation (6G) communication technology, global communication networks are moving towards the goal of comprehensive and seamless coverage. In particular, low earth orbit (LEO) satellites have become a critical component of satellite communication networks. The emergence of LEO satellites has brought about new co...

In the educational domain, identifying students at risk of dropping out is essential for allowing educators to intervene effectively, improving both academic outcomes and overall student well-being. Data in educational settings often originate from diverse sources, such as assignments, grades, and attendance records. However, most existing research...

Psychological resilience, defined as the ability to rebound from adversity, is crucial for mental health. Compared with traditional resilience assessments through self-reported questionnaires, resilience assessments based on neurological data offer more objective results with biological markers, hence significantly enhancing credibility. This paper...

Graph neural networks (GNNs) have emerged due to their success at modeling graph data. Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs come into play. To avoid communication caused by expensive data movement between workers, we propose Sancus and its advanced version Sancus, the staleness and quantizatio...

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we propose Romeo as the fir...

Segment Routing over IPv6 (SRv6) is an innovative and adaptable source routing technique that enhances inter-connection services. It plays a pivotal role in next-generation networking technologies, providing crucial support for network telemetry, computing power networks, and related technologies. The end-to-end connectivity capability of SRv6 is h...

In the fast-developing industrial environments, extensive focus on resource management within Mobile Edge Computing (MEC) aims to ensure low-latency QoS, however, some tasks offloaded to the cloud still experience high latency. Additionally, high energy consumption, poor link reliability, and excessive processing delays are intolerable for industri...

Recently, federated learning (FL) has become a promising distributed learning paradigm that caters to the recent trend of pushing intelligence from the cloud to the edge. Nevertheless, communication bottlenecks and device dropout can lead to inefficient FL in the large network scale, where massive devices cannot be accessed with severely limited ne...

Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a centralized framework, resulting in a high service delay and negative user experiences. However, not only does the workload of an AIGC task depend on the AIGC model's comple...

Traffic prediction plays a crucial role in intelligent transportation systems. Existing approaches primarily focus on improving overall accuracy, often neglecting a critical issue: whether predictive models lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors across urban areas results in imb...

The use of Artificial Intelligence (AI) has gained momentum in education. However, the use of AI in K-12 education is still in its nascent stages, and further research and development is needed to realize its potential. Moreover, the creation of a comprehensive and cohesive system that effectively harnesses AI to support teaching and learning acros...

Federated class-incremental learning (FCIL) allows multiple clients in a distributed environment to learn models collaboratively from evolving data streams, where new classes arrive continually at each client. Some existing works in FCIL combine traditional federated learning methods with class-incremental methods. However, the global model affecte...

Due to the contradiction between limited bandwidth and huge transmission parameters, federated Learning (FL) has been an ongoing challenge to reduce the model parameters that need to be transmitted to server in clients for fast transmission. Existing works that attempt to reduce the amount of transmitted parameters have limitations: 1) the reduced...

Network verification has recently made strides, focusing on the satisfiability of configurations and policies or the performance and versatility of their methods. However, they generally ignore explainability, which is the ability to explain why a network violates or satisfies a certain forwarding policy. In this paper, we propose an explainable ne...

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (C...

In multivariate time series (MTS) analysis, data loss is a critical issue that degrades analytical model performance and impairs downstream tasks such as structural health monitoring (SHM) and traffic flow monitoring. In real-world applications, MTS is usually collected by multiple types of sensors, making MTS and correlations between variates hete...

Despite advancements in Text-to-Video (T2V) generation, producing videos with realistic motion remains challenging. Current models often yield static or minimally dynamic outputs, failing to capture complex motions described by text. This issue stems from the internal biases in text encoding, which overlooks motions, and inadequate conditioning mec...

Explainable molecular property prediction is essential for various scientific fields, such as drug discovery and material science. Despite delivering intrinsic explainability, linear models struggle with capturing complex, non-linear patterns. Large language models (LLMs), on the other hand, yield accurate predictions through powerful inference cap...

Blockchain oracle is a critical third-party web service for Decentralized Finance (DeFi) protocols. Oracles retrieve external information such as token prices from exchanges and feed them as trusted data sources into smart contracts, enabling core DeFi applications such as loaning protocols. Currently, arithmetic mean based time-weighted average pr...

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic models, while precise, struggle with substructure heterogeneity and sparsity, whereas neural models, although effective, generally lack interpretability and cannot handl...

Satellite communication networks have attracted widespread attention for seamless network coverage and collaborative computing. In satellite-terrestrial networks, ground users can offload computing tasks to visible satellites that with strong computational capabilities. Existing solutions on satellite-assisted task computing generally focused on sy...

The proliferation of various mobile devices with massive data and improving computing capacity have prompted the rise of edge artificial intelligence (Edge AI). Without revealing the raw data, federated learning (FL) becomes a promising distributed learning paradigm that caters to the above trend. Nevertheless, due to periodical communication for m...

The blockchain-based metaverse has achieved great success through decentralized ownership of digital assets. However , the prevalence of Sybil attacks seriously threatens the security of assets, where attackers create multiple fake wallets to gain illegal benefits from metaverse activities such as token airdrops. Existing anti-Sybil mechanisms eith...

Digital Twin (DT) is increasingly being adopted into the metaverse to enhance its immersiveness by creating virtual representations of physical objects. A major unsolved challenge of this adoption is ensuring the trustworthiness of DT, which requires that the mapped virtual object is strictly consistent with its corresponding physical object. Howev...

Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction accuracy by inducing data drift...

NFC tag authentication is crucial for preventing tag misuse. Existing NFC fingerprinting methods use physical-layer signals, which incorporate tag hardware imperfections, for authentication purposes. However, these methods suffer from limitations such as low scalability for a large number of tags or incompatibility with various NFC protocols, hinde...

Video analytics at mobile edge servers offers significant benefits like reduced response time and enhanced privacy. However, guaranteeing various quality-of-service (QoS) requirements of dynamic video analysis requests on heterogeneous edge devices remains challenging. In this paper, we propose a scalable online video analytics scheme, called Novas...

In continual learning (CL), model growth enhances adaptability over new data, improving knowledge retention for more tasks. However, improper model growth can lead to severe degradation of previously learned knowledge, an issue we name as growth-induced forgetting (GIFt), especially in task-agnostic CL using entire grown model for inference. Existi...

Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteeing the data privacy of the trainers. FedAvg has become a foundational parameter update strategy for federated learning, which has been promising to eliminate the effect o...

As large language models (LLMs) appear to behave increasingly human-like in text-based interactions, more and more researchers become interested in investigating personality in LLMs. However, the diversity of psychological personality research and the rapid development of LLMs have led to a broad yet fragmented landscape of studies in this interdis...

Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving structure fairness in dynamic graph embedding remains an open problem. Neglecting degree changes in dynamic graphs will significan...

Importance Few studies have directly and objectively measured the individual and combined effects of multifaceted hand hygiene education programs. Objective To evaluate the individual and combined immediate effects of an instructional video and hand scan images on handwashing quality, decontamination, and knowledge improvement. Design, Setting, a...

In-band network telemetry (INT) allows for fine-grained network monitoring, without requiring communication with the controller at each hop. Existing INT-based network-wide telemetry systems achieve low-overhead monitoring with non-overlapping path planning algorithms. However, these systems do not constrain the length of the generated probing path...

With the development of network measurement technologies, a hybrid measurement architecture can effectively optimize the sketch structure in switches, making it more adaptable to the current complex and volatile network environment. However, current optimization technologies based on hybrid measurement architectures generally suffer from insufficie...

Decentralized task offloading among cooperative edge nodes has been a promising solution to enhance resource utilization and improve users’ Quality of Experience (QoE) in edge computing. However, current decentralized methods, such as heuristics and game theory-based methods, either optimize greedily or depend on rigid assumptions, failing to adapt...

Collaborative edge computing has become a popular paradigm where edge devices collaborate by sharing resources. Data dissemination is a fundamental problem in CEC to decide what data is transmitted from which device and how. Existing works on data dissemination have not focused on coflow scheduling in CEC, which involves deciding the order of flows...

Accurate surface roughness prediction is critical for ensuring high product quality, especially in areas like manufacturing and aerospace, where the smallest imperfections can compromise performance or safety. However, this is challenging due to complex, non-linear interactions among variables, which is further exacerbated with limited and imbalanc...

Large language models (LLMs) have shown great potential in natural language processing and content generation. However, current LLMs heavily rely on cloud computing, leading to prolonged latency, high bandwidth cost, and privacy concerns. Edge computing is promising to address such concerns by deploying LLMs on edge devices, closer to data sources....

Programmable switches allow data plane to program how packets are processed, which enables flexibility for network management tasks, e.g., packet scheduling and flow measurement. Existing studies focus on program deployment at a single switch, while deployment across the whole data plane is still a challenging issue, especially manifested in the di...

Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, whic...

Collaborative edge computing (CEC) is an emerging computing paradigm in which edge nodes collaborate to perform tasks from end devices. Task offloading decides when and at which edge node tasks are executed. Most existing studies assume task profiles and network conditions are known in advance, which can hardly adapt to dynamic real-world computati...

Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy, parallel split learning is becoming a practical and popular approach. However, current parallel spl...

Due to the ever-growing powers in sensing, computing, communicating and storing, mobile devices (e.g., smartphone, smartwatch, smart glasses) become ubiquitous and an indispensable part of people’s daily life. Until now, mobile devices have been adopted in many applications, e.g., exercise assessment, daily life monitoring, human-computer interacti...

One of the promises of edu-metaverse is its ability to provide a virtual environment that enables us to engage in learning activities that are similar to or on par with reality. The digital enhancements introduced in a virtual environment contribute to our increased expectations of novel learning experiences. However, despite its promising outcomes...

This paper investigates the problem of learning privacy-preserving graph representations with graph neural networks (GNNs). Different from existing works based on adversarial training, we introduce a variational approach, called vGPF, to encourage the isolation of sensitive attributes from the learned representations. Specifically, we first formula...

Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. However, traditional DRL solu...

Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consuming, making the reduction of network distance measurement costs a top priority. The tensor completion method efficiently reduces measur...

For the creation of wireless network applications, node locations are frequently necessary. However, communication effectiveness, measurement accuracy, and localization stability will be low in irregular multi-hop networks when locating nodes using conventional algorithms. To this end, a novel cooperative localization algorithm using expected minim...

With the rapid growth in data center network bandwidth far outpacing improvements in CPU performance, traditional software middleboxes running on servers have become inefficient. The emerging data processing units aim to address this by offloading network functions from the CPU. However, as DPUs are still a new technology, there lacks comprehensive...

Accurate prediction of ocean factors (e.g., temperature and salinity) is crucial for plenty of applications, including weather forecasting, storm tracking, and ecosystem protection. Meanwhile, it is well-known that the ocean is a unified system and various ocean factors usually influence each other. For example, the changes in temperature would aff...

Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attac...

Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute tasks from end devices. Task offloading is a fundamental problem in CEC that decides when and where tasks are executed upon the arrival of tasks. However, the mobility of users often results in unstable connections, leading to netw...

Network measurement is critical for various network applications, but scaling measurement techniques to the network-wide level is challenging for existing sketch-based solutions. In software switches, centralized deployment provides low resource usage but suffers from poor load balancing. In contrast, collaborative measurement achieves load balanci...

Large language models (LLMs) have shown great success in content generation and intelligent intelligent decision-making for IoT systems. Traditionally, LLMs are deployed on the cloud, incurring prolonged latency, high bandwidth costs, and privacy concerns. More recently, edge computing has been considered promising in addressing such concerns becau...

Managing heterogeneous datasets that vary in complexity, size, and similarity in continual learning presents a significant challenge. Task-agnostic continual learning is necessary to address this challenge, as datasets with varying similarity pose difficulties in distinguishing task boundaries. Conventional task-agnostic continual learning practice...

Metaverse, an alternative universe for play, work and interaction, has become a captivating topic for academia and industry in recent times. This opens the question on what a metaverse for education, or edu-metaverse, should look like. It is believed that this metaverse for learning should be grounded by a pedagogical theory. Particularly, we propo...

In the rapidly evolving educational landscape, the integration of metaverse and gamification is emerging as a revolutionary approach. This paper presents the Gamified Constructivist Teaching in the Metaverse (GCTM) framework, aiming to enhance engagement and satisfaction in the computer science education domain. Implemented in two engineering class...

High myopia (HM) is a leading cause of irreversible vision loss due to its association with various ocular complications including myopic maculopathy (MM). Visual field (VF) sensitivity systematically quantifies visual function, thereby revealing vision loss, and is integral to the evaluation of HM-related complications. However, measuring VF is su...

Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolutio...

Personality recognition in text is a critical problem in classifying personality traits from the input content of users. Recent studies address this issue by fine-tuning pre-trained language models (PLMs) with additional classification heads. However, the classification heads are often insufficiently trained when annotated data is scarce, resulting...

Background Few studies have investigated how the effectiveness of hand washing in removing hand contaminants is influenced by the performance and duration of each step involved. We conducted an observational study by recruiting participants from a university campus, with the aim to comprehensively evaluate how performance, duration and demographic...

Estimating engineering structures’ health conditions and predicting their future behaviors are fundamental problems for a city’s safe and efficient operations. Data-driven solutions estimate the health conditions using statistical models generated from measurement data. They have attracted growing interest recently because advances in information a...

Manipulator teams are frequently employed in various industrial applications to handle challenging cooperative tasks. The complicated interaction between manipulators makes it difficult to design applications from scratch. Although robotics middleware has emerged as the key to lowering the development complexity of manipulator applications, existin...

Glaucoma, which makes progressive and irreversible sight damage to human eyes, is the second leading cause of blindness worldwide. The damage is principally estimated by visual field (VF) sensitivity through costly visual field tests. To achieve a less costly estimation, a promising method is to first measure retinal layers thickness (RT) by optica...

Early detection of fatigue driving is pivotal for safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body....

Graph neural networks (GNNs) have emerged due to their success at modeling graph data. Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs come into play. To avoid communication caused by expensive data movement between workers, we propose SANCUS, a staleness-aware communication-avoiding decentralized GNN sy...

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., dive...

Satellite communication networks have attracted widespread attention for seamless network coverage and collaborative computing. In satellite-terrestrial networks, ground users can offload computing tasks to visible satellites that with strong computational capabilities. Existing solutions on satellite-assisted task computing generally focused on sy...

Motivated by the successful applications of commonsense knowledge graphs (KGs) and encyclopedia KGs, many KG-based applications have been developed in education, such as course content visualization and learning path/material recommendations. While KGs for education are often constructed manually, attempts have been made to leverage machine learnin...

The rapid emergence of knowledge graph (KG) research opens the opportunity for revolutionary educational applications. Most studies in this area use KGs as peripheral sources of educational materials rather than a primary tool for Instructional Design. Considerable effort is required to maintain the alignment between KGs and other elements of Instr...

The academic success of students can be improved by an understanding of the academic domain they are navigating. As such, they may benefit from gaining valuable perspective into the shape of their chosen field via an enhanced visual aid. We discuss K-Cube VR, a work-in-progress academic domain browser, which provides visualization of such informati...

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product reco...

Writing a survey paper on one research topic usually needs to cover the salient content from numerous related papers, which can be modeled as a multi-document summarization (MDS) task. Existing MDS datasets usually focus on producing the structureless summary covering a few input documents. Meanwhile, previous structured summary generation works fo...

Automatic document summarization aims to produce a concise summary covering the input document's salient information. Within a report document, the salient information can be scattered in the textual and non-textual content. However, existing document summarization datasets and methods usually focus on the text and filter out the non-textual conten...

Vibration measurement is vital for fault diagnosis of structures (e.g., machines and civil structures). Different structure components undergo distinct vibration patterns, which jointly determine the structure's health condition, thus demanding simultaneous multi-point vibration monitoring. Existing solutions deploy multiple accelerometers along wi...