Jiannong Cao's research while affiliated with The Hong Kong Polytechnic University and other places

Publications (692)

Preprint
Neural networks tend to forget previously learned knowledge when continuously learning on datasets with varying distributions, a phenomenon known as catastrophic forgetting. More significant distribution shifts among datasets lead to more forgetting. Recently, parameter-isolation-based approaches have shown great potential in overcoming forgetting...
Preprint
Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly facilitates our lives by providing various rapid-response services. Due to the limited resources of IoT devices, cloud-assisted training of CNN models has become the mainstr...
Conference Paper
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...
Preprint
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...
Preprint
Full-text available
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...
Preprint
Full-text available
Multi-agent reinforcement learning (MARL) has been gaining extensive attention from academia and industries in the past few decades. One of the fundamental problems in MARL is how to evaluate different approaches comprehensively. Most existing MARL methods are evaluated in either video games or simplistic simulated scenarios. It remains unknown how...
Article
Full-text available
Online forumpost evaluationis an effective way for instructors to assess students’ knowledge understanding and writing mechanics. Manually evaluating massive posts costs a lot of time. Automatically grading online posts could significantly alleviate instructors’ burden. Similar text assessment tasks like Automated Text Scoring evaluate the writing...
Article
ive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucia...
Article
In real life, people often participate in activities in groups. During the activities, group members commonly engage in interactions such as shaking hands, waving hands, embracing, and hooking arms. Existing approaches to recognize human groups assume that the individuals’ locations or sensing signals are similar; the interactions among them are pr...
Article
Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based...
Conference Paper
Full-text available
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...
Article
Multi‐robot systems are widely used to handle complex and cooperative missions in various industrial applications. Although robotic middleware has become the key to reducing the complexity of multi‐robot application development, existing works still have limitations in controlling multiple robots to perform missions cooperatively. To enable multi‐r...
Article
User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication....
Article
Unsupervised image-to-image translation aims to learn the mapping from an input image in a source domain to an output image in a target domain without paired training dataset. Recently, remarkable progress has been made in translation due to the development of generative adversarial networks (GANs). However, existing methods suffer from the trainin...
Article
Full-text available
Imbalanced data causes deep neural networks to output biased results, and it becomes more serious when facing extremely imbalanced data regarding the outliers with tiny size (the ratio of the outlier size to the image size is around 0.05%). Many data argumentation models are proposed to supplement imbalanced data to alleviate biased results. Howeve...
Article
Pen-based handwriting has become one of the major human-computer interaction methods. Traditional approaches either require writing on the specific supporting device like the touch screen, or limit the way of using the pen to pure rotation or translation. In this paper, we propose Handwriting-Assistant, to capture the free handwriting of ordinary p...
Article
Abnormal traffic incidents such as traffic accidents have become a significant health and development threat with the rapid urbanization of many countries. The challenges of accurate traffic risk forecasting are three-fold. First, traffic accident data in some areas of a city is sparse, especially for a fine-grained prediction, which may cause the...
Article
Internet of Things (IoT) has found increasing applications in industry, including in the Building Automation field. Edge computing, a distributed computing paradigm using the computing resources of edge devices that are close to the sources of data, is an effective means for dealing with the network traffic caused by the centralized structure and r...
Conference Paper
Full-text available
In recent years, blockchain technology has been attracting intensive attention from both the industries and academia because of its capability of rebuilding trust in trustless environments. There are increasing demands for developing and delivering blockchain applications and services in an agile and continuous way. To this end, Blockchain as a Ser...
Preprint
Human behavior modeling deals with learning and understanding of behavior patterns inherent in humans' daily routines. Existing pattern mining techniques either assume human dynamics is strictly periodic, or require the number of modes as input, or do not consider uncertainty in the sensor data. To handle these issues, in this paper, we propose a n...
Article
Respiration monitoring (RM) is crucial for tracking various health problems. Recently, RFID has been widely employed for lightweight and low-cost RM. However, existing RFID-based RM systems are designed for static environments where no people move around the monitored person. While, in practice, most environments are dynamic with people moving near...
Article
The human imperceptible adversarial examples crafted by ℓ0-norm attacks, which aims to minimize ℓ0 distance from the original image, thereby misleading deep neural network classifiers into the wrong classification. Prior works of tackling ℓ0 attacks can neither eliminate perturbed pixels nor improve the performance of the classifier in the recovere...
Article
A future Internet of Things (IoT) will feature a service-oriented architecture consisting of lightweight computing platforms offering individual, loosely-coupled microservices. Often, an end-user will request a bespoke service that will require a composition of two or more microservices offered by different service providers. However, the underlyin...
Article
Byzantine Fault Tolerant (BFT) state machine replication protocols are used to achieve agreement among replicated servers with arbitrary faults. Most existing BFT protocols perform well in fault-free cases, but usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes gracefu...
Preprint
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Data-driven approaches have been applied to many problems in urban computing. However, in the research community, such approaches are commonly studied under data from limited sources, and are thus unable to characterize the complexity of urban data coming from multiple entities and the correlations among them. Consequently, an inclusive and multifa...
Article
Dynamic graph embedding learns representation vectors for vertices and edges in a graph that evolves over time. We aim to capture and embed the evolution of vertices' temporal connectivity. Existing work studies the vertices' dynamic connection changes but neglects the time it takes for edges to evolve, failing to embed temporal linkage information...
Article
Domain knowledge informed machine learning Domain knowledge informed artificial intelligence Optimization in port state control (PSC) PSCO scheduling model Inspection template a b s t r a c t Maritime transportation is the backbone of global supply chain. To improve maritime safety, protect the marine environment, and set out seafarers' rights, por...
Article
Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inap...
Article
Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppli...
Chapter
This paper presents a simple broadcast operation suited to n-process asynchronous message-passing systems in which (i) up to t processes may commit Byzantine faults, and (2), while the underlying communication network is connected (any pair of processes is connected by a path), not all the pairs of processes are directly connected by a communicatio...
Chapter
Full-text available
In the big data era, with the large volume of available data collected by various sensors deployed in urban areas and the recent advances in AI techniques, urban computing has become increasingly important to facilitate the improvement of people’s lives, city operation systems, and the environment. In this chapter, we introduce the challenges, meth...
Article
This paper presents the design and implementation of a low-cost software-defined RFID system for distributed parallel sensing. We aim to implement essential sensing functionalities with low-cost commodity radio components and provide full access to physical layer raw data (e.g., PHY samples of backscatter signals) to enable various RFID sensing app...
Article
Accurately predicting the urban spatio-temporal data is critically important to various urban computing tasks for smart city related applications such as crowd flow prediction and traffic congestion prediction. Existing models especially deep learning based approaches require a large volume of training data, whose performance may degrade remarkably...
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Full-text available
Network embedding aims to learn a low-dimensional representation vector for each node while preserving the inherent structural properties of the network, which could benefit various downstream mining tasks such as link prediction and node classification. Most existing works can be considered as generative models that approximate the underlying node...
Article
Traditionally, Artificial Intelligence (AI) models are trained on the central cloud with data collected from end devices. This leads to high communication cost, long response time, and privacy concerns. Recently Edge-empowered AI, namely, Edge AI, has been proposed to support AI model learning and deployment at the network edge closer to the data s...
Article
Computation partitioning is an important technique to improve the application performance by selectively offloading some computations from the mobile devices to the nearby edge cloud. In a dynamic environment in which the network bandwidth to the edge cloud may change frequently, the partitioning of the computation needs to be updated accordingly....
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Full-text available
In recent years, blockchain has been broadly applied to industrial Internet of things (IIoT) due to its features of decentralization, transparency, and immutability. In existing permissioned blockchain based IIoT solutions, transactions submitted by IIoT devices are arbitrarily packed into blocks without considering their waiting times. Hence, ther...
Article
Full-text available
Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a device at multi-hop distance leading to conflicting...
Article
Social voting is an emerging new feature in online social platforms, through which users can express their attitudes and opinions towards various interested subjects. Since both social relations and textual content decide the votes propagation, the diverse sources present opportunities and challenges for recommender systems. In this paper, we joint...
Article
Generative Adversarial Network (GAN) is a thriving generative model and considerable efforts have been made to enhance the generation capabilities via designing a different adversarial framework of GAN (e.g., the discriminator and the generator) or redesigning the penalty function. Although existing models have been demonstrated to be very effectiv...
Article
Full-text available
Collaborative edge computing (CEC) is a recently popular paradigm enabling sharing of data and computation resources among different edge devices. Task offloading is an important problem to address in CEC as we need to decide when and where each task is executed. However, it is challenging to solve task offloading in CEC as tasks can be offloaded t...
Article
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smar...
Chapter
Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes and lead to unsatisfying prediction performance. We propose a n...
Chapter
Resource management becomes a critical issue in airport operation since passenger throughput grows rapidly but the fixed resources such as baggage carousels hardly increase. We propose a Big-data-driven Airport Resource Management (BigARM) engine and develop a suite of application tools for efficient resource utilization and achieving customer serv...
Preprint
Traditionally, AI models are trained on the central cloud with data collected from end devices. This leads to high communication cost, long response time and privacy concerns. Recently Edge empowered AI, namely Edge AI, has been proposed to support AI model learning and deployment at the network edge closer to the data sources. Existing research in...
Article
Most analyses on functional brain connectivity across a group of brains are under the assumption that the positions of the voxels are aligned into a common space. However, the alignment errors are inevitable. To address this issue, the distributional representation avoids the alignment in such a way that the spatial structure of connectivity is cap...
Chapter
Learning analytics is the measurement, collection, and analysis of data about learners and their contexts for the purposes of understanding and optimizing the process of learning and the underlying environment. Due to the complex nature of the learning process, existing works mostly focus on the modeling and analysis of single learning behavior and...
Conference Paper
Full-text available
Respiration monitoring (RM) is essential for diagnosing and tracking respiratory diseases. Recently, RFID technology has enabled RM in a lightweight and cost-effective way by only attaching the tiny and cheap RFID tag on the monitored person's chest. However, current systems are mostly designed for static environments with no surrounding people's m...
Article
Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources are shared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications, multiple application queries can exhibit overlapping in their functional requirements, such as the region of interest, sensing attributes...
Preprint
Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes and lead to unsatisfying prediction performance. We propose a n...
Article
Citywide crowd flow data are ubiquitous nowadays, and forecasting the flow of crowds is of great importance to many real applications such as traffic management and mobility-on-demand (MOD) services. The challenges of accurately predicting urban crowd flows come from both the nonlinear spatial-temporal correlations of the crowd flow data and the co...
Article
Group detection is gaining popularity as it enables various applications ranging from marketing to urban planning. Existing methods use received signal strength indicator (RSSI) to detect co-located people as groups. However, this approach might have difficulties in crowded urban spaces since many strangers with similar mobility patterns could be i...
Article
Generative Adversarial Network (GAN) has been widely used to generate impressively plausible data. However, it is a non-trivial task to train the original GAN model in practice due to the vanishing gradient problem. This is because the JS divergence could be a constant (i.e., log2) when original data distribution and generated data distribution hol...
Article
A successive similar pattern (SSP) is a series of similar sequences that occur consecutively at non-regular intervals in time series. Mining SSPs could provide valuable information without a priori knowledge, which is crucial in many applications ranging from health monitoring to activity recognition. However, most existing work is computationally...
Article
Full-text available
Network embedding has been increasingly employed in network analysis as it can learn node representations that encode the network structure resulting from node interactions. In this paper, we propose to embed not only the network structure, but also the interaction content within which each interaction arises. The interaction content should better...
Preprint
Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to in...
Article
Full-text available
Live Virtual Machine (VM) migration among geographically distributed edge clouds is an important strategy for providing low latency and reliable services for mobile end users. VM migration among edge clouds is more challenging than that in cloud computing, because the network bandwidth among edge clouds is more constrained than the cloud data cente...
Article
The formation control of mobile underwater wireless sensor networks (MUWSNs) is difficult due to the severe errors in distance and motion measurements. To address this problem, we propose a distributed formation control scheme, TRiForm (Triangle Formation). TRiForm constructs a virtual structure that is a rigid graph formed by triangles of nodes in...
Article
Trustworthiness is the probability that a system will function according to intended behaviors under a set of circumstances as demonstrated by qualities including, but not limited to safety, security, privacy, reliability, real timeliness. Trustworthiness in the industrial Internet of Things (IIoT) systems and applications is crucial to a vital exp...
Article
Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources areshared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications,multiple application queries can exhibit overlapping in their functional requirements, such as the region ofinterest, sensing attributes, a...
Article
Unknown RFID tags appear when tagged items are not scanned before being moved into a warehouse, which can even cause serious security issues. This paper studies the practically important problem of unknown tag detection. Existing solutions either require low-cost tags to perform complex operations or beget a long detection time. To this end, we pro...
Article
Integrated GPS receivers have become a basic module in today’s mobile devices. While serving as the cornerstone for location based services, GPS modules have a serious battery drain problem due to high computation load. This paper aims to reveal the impact of key software parameters on GPS energy consumption, by establishing an energy model for a s...