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January 2019 - October 2019
August 2006 - August 2015
May 2001 - August 2006
Publications
Publications (313)
Because of their proficiency in capturing the category characteristics of graphs, graph neural networks have shown remarkable advantages for graph-level classification tasks, that is, rumor detection and anomaly detection. Due to the manipulation of special means (e.g. bots) on online media, rumors may spread across the whole network at an overwhel...
Blockchain is a revolutionary technology that has reshaped the trust model among mutually distrustful peers in a distributed network. While blockchain is well-known for its initial usage in a public manner, such as the cryptocurrency of Bitcoin, consortium blockchain, which requires authentication of all involved participants, has also been widely...
Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of machinery and equipment in industrial environments. With the wide deployment of multimodal sensors and the rapid development of Internet of Things (IoT), the data generated in modern industrial production has become increasingly diverse and complex. However...
Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this paper, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unm...
The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mec...
As software systems become increasingly large and complex, automated parameter tuning of software systems (PTSS) has been the focus of research and many tuning algorithms have been proposed recently. However, due to the lack of a unified platform for comparing and reproducing existing tuning algorithms, it remains a significant challenge for a user...
Deep learning (DL) has become a dominating type of workloads on AI computing platforms. The performance of such platforms highly depends on how distributed DL jobs are scheduled. Reinforcement learning (RL)-based schedulers have been extensively studied and are capable of modeling interferences between concurrent jobs competing for resources. Howev...
Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progr...
For fresh table grapes, flavor is one of the most important components of their overall quality. The flavor of table grapes includes both their taste and aroma, involving multiple physical and chemical properties, such as soluble solids. In this paper, we investigate six factors, divide flavor ratings into a range of five grades based on the result...
Configuration tuning for large software systems is generally challenging due to the complex configuration space and expensive performance evaluation. Most existing approaches follow a two-phase process, first learning a regression-based performance prediction model on available samples and then searching for the configurations with satisfactory per...
With the recent development of AIoT (AI+IoT), crowdsourcing has emerged as a promising paradigm for distributed problem solving and business practice. Crowdsourcing entails posting tasks on a dedicated web platform, enabling networked workers to choose preferred tasks on a first-come, first-served basis, typically of the same type to ensure high as...
In recent years, we have witnessed an exorbitant growth of online interactions, especially during the pandemic of COVID-19, which requires diversified digital agreements in different application scenarios. In essence, multi-party agreement signing (MPAS) can be reckoned as a special case of the multi-party fair exchange (MPFE) for signatures over a...
Many modern software systems provide numerous configuration options to users and different configurations often lead to different performances. Due to the complex impact of a configuration on the system performance, users have to experimentally evaluate the performance for different configurations. However, it is practically infeasible to exhaust t...
Fatigue detection for air traffic controllers is an important yet challenging problem in aviation safety research. Most of the existing methods for this problem are based on facial features. In this paper, we propose an ensemble learning model that combines both facial features and voice features and design a fatigue detection method through multif...
As secure messaging services become ubiquitous, the need for moderation tools that can function within these systems without defeating their purpose becomes more and more pressing. There are several solutions to deal with moderation on a local level, handling harassment and personal-scale issues, but handling wider-scale issues like disinformation...
Gamma radiation has been classified by the International Agency for Research on Cancer (IARC) as a carcinogenic agent with sufficient evidence in humans. Previous studies show that some weather data are cross-correlated with gamma exposure rates; hence, we hypothesize that the gamma exposure rate could be predicted with certain weather data. In thi...
The widespread deployment of scientific applications and business services of various types on clouds requires the transfer of big data with different priorities between geographically distributed cloud-based data centers. As a result, Cloud Service Providers (CSP) face a significant challenge to fully utilize the expensive bandwidth resources of t...
Vehicular ad-hoc networks (VANETs) aim to provide a comfortable driving experience. Sharing messages in VANETs can help with traffic management, congestion mitigation, and driving safety. However, forged or false messages may undermine the efficiency of VANETs. In this paper, we propose a security scheme based on blockchain technology, where two ty...
Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing...
Resource provisioning for cloud computing requires adaptive and accurate prediction of cloud workloads. However, existing studies in workload prediction have faced significant challenges in predicting time-varying cloud workloads of diverse trends and patterns, and the lack of accurate prediction often results in resource waste and violation of Ser...
Vehicular social networks are emerging hybrid networks that combine traditional vehicular networks and social networks, with two key types of nodes, that is, vehicles and drivers. Since vehicle behaviors are controlled or influenced by drivers, the trustworthiness of a vehicle node is essentially determined by its own communication behaviors and it...
Gesture detection based on RF signals has gained increasing popularity in recent years due to several benefits it has brought such as eliminating the need to carry additional devices and providing better privacy. In traditional methods, significant breakthroughs have been made to improve recognition accuracy and scene robustness, but the limited co...
Due to the rapidly growing volume and velocity of big data, real-time streaming data analysis has become increasingly important in many applications. To discover knowledge from such data, a wide range of machine learning techniques have been proposed and used in practice. Among them, clustering, which aims at grouping objects into different classes...
Peer-to-peer (P2P) content delivery is up-and-coming to provide benefits comprising cost-saving and scalable peak-demand handling compared with centralized content delivery networks (CDNs), and also complementary to the popular decentralized storage networks such as Filecoin. However, reliable P2P delivery demands proper enforcement of delivery fai...
Many big data applications such as smart transportation, healthcare, and e-commerce need to store and query large collections of small XML documents, which has become a fundamental problem. However, existing solutions are inadequate to deliver satisfactory query performance in such circumstances. In this paper, we propose a framework named XML2HBas...
In comparison with conventional content delivery networks, peer-to-peer (p2p) content delivery is promising to save cost and handle high peak-demand, and can also complement the decentralized storage networks such as Filecoin. However, reliable p2p delivery requires proper enforcement of delivery fairness, i.e., the deliverers should be rewarded ac...
With the rapid increase in GPS data collection through pervasive use of mobile devices, it has become an important problem to discover significant places of moving objects from complex spatial and temporal trajectories. This problem is challenging mainly because such trajectory data suffer from several issues including incompleteness, low quality,...
Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwid...
Multi-user concurrent communication has become an increasingly important issue in mobile computing. Emerging vibration-based communication provides a new multi-to-single (M2S) data communication model for face-to-face tasks as opposed to most existing device-to-device (D2D) communication technologies. However, the transmission of multiple concurren...
Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP‐Growth have been proposed. FP‐Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity...
The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional dis...
Peer-to-peer (p2p) content delivery is promising to reduce the cost of traditional CDNs and complement the decentralized storage networks such as Filecoin. However, reliable p2p delivery requires proper enforcement of delivery fairness, i.e., the deliverers should be rewarded according to their in-time delivery. Unfortunately, most existing studies...
The over operator is commonly used for α-blending in various visualization techniques. In the current form, it is a binary operator and must strictly follow a specific composition order of all participating operands, hence posing a significant performance limit. In this paper, we derive a set of generic formulas for the over operator that work with...
Traffic sub-area division plays an important role in traffic control and is a critical task for traffic system management and traffic network analysis. Most existing algorithms for traffic sub-area division are based on traffic road networks and face a significant challenge in dealing with complex and time-varying traffic network conditions. This p...
Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more...
Big data processing and analysis increasingly rely on workflow technologies for knowledge discovery and scientific innovation. The execution of big data workflows is now commonly supported on reliable and scalable data storage and computing platforms such as Hadoop. There are a variety of factors affecting workflow performance across multiple layer...
Traffic information of disparate types in Internet of Vehicles (IoV) is the basis for supporting various IoV applications. Since traffic flows vary in real time, it is challenging to carry out efficient and reliable transfer of such information. Moreover, IoV is vulnerable to security threats due to its inherent properties such as dynamically chang...
In general, rich media applications demand high bandwidth to transfer large files over wide-area connections. The arrival of 5G will overcome the limitations of existing networks in support of these network-intensive applications because they provide high bandwidth reaching up to multiple Gbps for large file transfer. One of the main challenges to...
Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate ba...
There is a rapidly growing need for processing large volumes of streaming data in real time in various big data applications. As one of the most commonly used systems for streaming data processing, Apache Storm provides a workflow-based mechanism to execute directed acyclic graph (DAG)-structured topologies. With the expansion of cloud infrastructu...
Network Intrusion Detection System (NIDS) is a key security device in modern networks to detect malicious activities. However, the problem of imbalanced class associated with intrusion detection dataset limits the classifier’s performance for minority classes. To improve the detection rate of minority classes while ensuring efficiency, we propose a...
Many large-scale applications in broad science, engineering, and business domains are generating big data, which must be transferred to remote sites for various storage and analysis purposes. Bandwidth reservation services that discover feasible routing options over dedicated paths in high-performance networks have proved to be effective for such b...
Multi-radio multi-channel (MRMC) wireless mesh networks (WMNs) hold the promise to become the next-generation networks to provide the service of ubiquitous computing, access to the Internet, and support for a large number of data flows. Many applications in WMNs can be modeled as a multi-flow coexistence problem. Assembling links distributed in ort...
Multi-radio multi-channel (MRMC) wireless mesh networks (WMNs) have emerged as the broadband networks to provide access to the Internet for ubiquitous computing with the support for a large number of data flows. Many applications in WMNs can be abstracted as a multi-flow coexistence problem to carry out multiple concurrent data transfers. More spec...
An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SD...
Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in v...
In the data mining of road networks, trajectory clustering of moving objects is of particular interest for its practical importance in many applications. Most of the existing approaches to this problem are based on distance measurement, and suffer from several performance limitations including inaccurate clustering, expensive computation, and incom...
Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address limitations faced by centralized systems. While many existing efforts are successful in decentralization with multiple servers (i.e., full nodes) to handle faulty nodes, an important issue has arisen that external client...
Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ a z-score scheme for uniformity and design...
To protect sensitive information of patients and prevent privacy leakage, it is necessary to deal with data anonymously in medical dataset publishing. Most of the existing anonymity protection technologies discard the records with missing data, and it will cause large differences in characteristics in data anonymization, resulting in severe informa...
In recent years, cloud platforms have been rapidly developed and deployed around the globe and many large-scale scientific workflows have been migrated to multiple clouds for cost-effective data analysis. In such cloud-based workflow applications, financial cost is a major concern in addition to traditional performance requirements such as executio...
Microservice has been increasingly recognized as a promising architectural style for constructing large-scale cloud-based applications within and across organizational boundaries. This microservice-based architecture greatly increases application scalability, but meanwhile incurs an expensive performance overhead, which calls for a careful design o...
Radiation source detection is an important problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple "aggregated...
This book presents a compilation of selected papers from the 2nd International Conference on Healthcare Science and Engineering (Healthcare 2018). The work focuses on novel computing, networking, and data analytics techniques for various issues in healthcare. The book is a valuable resource for academic researchers and practitioners working in the...
Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address some limitations faced by centralized systems. While many existing efforts are successful in leveraging blockchain for decentralization with multiple servers (full nodes) to handle faulty nodes, an important issue has ar...
Vehicular ad-hoc networks (VANETs) are prone to various network attacks due to its open, dynamic, and distributed nature. It is of increasing importance to assess the trustworthiness of vehicle nodes and messages for communication security in such networks. In this paper, we propose an integrated security scheme to help nodes in VANETs identify the...
