Phi Le Nguyen

Hanoi University of Science and Technology · School of Information and Communication Technology

Global air pollution is becoming increasingly severe. In this context, monitoring air quality at all times and locations is necessary. Traditionally, air quality is monitored using stationary monitoring stations. However, this approach has an inherent shortcoming: limited monitoring locations. Crowdsensing-based air monitoring has recently emerged...

Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern that needs to be investigated thoroughly. Recently, several attempts have been made to exploit deep learning to tackle the pill identifi...

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study exp...

In real-life conversations, meetings, or debates, there are often situations where many people speak at the same time, leading to overlapping speech segments. Such overlapping speech is an extremely challenging problem for the speaker diarization task. The widely used clustering-based diarization approaches perform quite poorly under such situation...

In the fields of optical character recognition and textual information extraction, document localization is recognized as a potential preprocessing step with a significant impact on accuracy. Despite numerous solutions being presented, localizing documents in images with complicated backgrounds remains an open issue. This paper offers a novel appro...

Representation learning for time series has been an important research area for decades. Since the emergence of the foundation models, this topic has attracted a lot of attention in contrastive self-supervised learning, to solve a wide range of downstream tasks. However, there have been several challenges for contrastive time series processing. Fir...

Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, especially backdoor insertion during training. Recently, the edge-case backdoor attack employing the tail of the data distribution has been proposed as a powerful one, rai...

Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern needed to be investigated thoroughly. Recently, several attempts have been made to exploit deep learning to tackle the pill identificati...

Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy. However, the heterogeneous distribution of data among clients in FL can make it difficult for the orchestration server to validate the integrity of local model updates, making FL vulnerable to various threats, i...

Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they fail to handle novel instances of pill categories that are frequently presented to the learning algorithm. To this end, a trivia...

Federated learning enables edge devices to train a global model collaboratively without exposing their data. Despite achieving outstanding advantages in computing efficiency and privacy protection, federated learning faces a significant challenge when dealing with non-IID data, i.e., data generated by clients that are typically not independent and...

Wireless Rechargeable Sensors Network (WRSN) has recently emerged as a promising solution to solve the energy limitation of WRSN. This study considers large-scale WRSNs, where many Mobile Chargers (MCs) are placed to ensure the target coverage and connectivity. We propose a distributed charging algorithm that allows MCs to decide their optimal char...

In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to use large-capacity neural networks in pursuit of high performance. On the other hand, deep convolutional neural networks (CNNs) are exceedingly parameter-hungry, which makes memory a significant bottleneck when training large-scale CNNs on hardware-const...

Internet of Things technology was introduced to allow many physical devices to connect over the Internet. The data and tasks generated by these devices put pressure on the traditional cloud due to high resource and latency demand. Vehicular Fog Computing (VFC) is a concept that utilizes the computational resources integrated into the vehicles to su...

Time series prediction, which obtains historical data of multiple features to predict values of features of interest in the future, is widely used in many fields. One of the critical issues in dealing with the time series prediction task is how to choose appropriate input features. This paper proposes a novel approach to select a sub-optimal featur...

This study focuses on MEC-enhanced, vehicle-based crowdsensing systems that rely on devices installed on automobiles. We investigate an opportunistic communication paradigm in which devices can transmit measured data directly to a crowdsensing server over a 4G communication channel or to nearby devices or so-called Road Side Units positioned along...

Multipath QUIC (MPQUIC), an emerging multipath transport protocol (MTP) that inherits the advantages of the canonical multipath TCP (MPTCP) and the widespread QUIC, potentially plays a vital role in 5G and beyond. MPQUIC can exploit multiple networks (e.g., Wi-Fi, LTE, 5G) on a mobile device to boost the quality of services while efficiently utiliz...

In many healthcare applications, identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the deep learning-based approach to tackle the pill recognition problem in the literature. However, due to the high similarity between pills...

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients...

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learnin...

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emp...

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the...

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) multi-order dynamics, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) internal dy...

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks , in which the attackers inject fake data to manipulate recommendation results as they desire. This migh...

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks, in which the attackers inject fake data to manipulate recommendation results as they desire. This might...

Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they fail to handle novel instances of pill categories that are frequently presented to the learning algorithm. To this end, a trivia...

Computer systems hold a large amount of personal data over decades. On the one hand, such data abundance allows breakthroughs in artificial intelligence (AI), especially machine learning (ML) models. On the other hand, it can threaten the privacy of users and weaken the trust between humans and AI. Recent regulations require that private informatio...

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the...

Wireless sensor networks consist of randomly distributed sensor nodes for monitoring targets or areas of interest. Maintaining the network for continuous surveillance is a challenge due to the limited battery capacity in each sensor. Wireless power transfer technology is emerging as a reliable solution for energizing the sensors by deploying a mobi...

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel...

Identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the deep learning-based approach to tackle the pill recognition problem in the literature. However, due to the high similarity between pills' appearance, misrecognition ofte...

In Wireless Rechargeable Sensor Networks (WRSNs), charging scheme optimization is one of the most critical issues, which plays an essential role in deciding the sensors’ lifetime. An effective charging scheme should simultaneously consider both the charging path and the charging time. Existing works, however, mainly focus on determining the optimal...

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subseque...

With the recent explosive growth of mobile devices such as smartphones or tablets, guaranteeing consistent web appearance across all environments has become a significant problem. This happens simply because it is hard to keep track of the web appearance on different sizes and types of devices that render the web pages. Therefore, fixing the incons...

In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortions or irregular layout. Most o...

Multipath communication is a well-developed technology that enhances communication effectiveness and resilience. Moreover, it can flexibly utilize network resources through load balancing among available paths. However, traditionally, deploying such load balancing functions on network devices is costly due to the required configuration changes and...

Monitoring air quality plays a critical role in the sustainable development of developing regions where the air is severely polluted. Air quality monitoring systems based on static monitors often do not provide information about the area each monitor represents or represent only small areas. In addition, they have high deployment costs that reflect...

With the recent explosive growth of mobile devices such as smartphones or tablets, guaranteeing consistent web appearance across all environments has become a significant problem. This happens simply because it is hard to keep track of the web appearance on different sizes and types of devices that render the web pages. Therefore, fixing the incons...

This paper addresses the local minimum phenomenon, routing path enlargement, and load imbalance problems of geographic routing in wireless sensor networks (WSNs) with holes. These issues may degrade the network lifetime of WSNs since they cause a long detour path and a traffic concentration around the hole boundary. Aiming to solve these problems,...

In wireless rechargeable sensor networks (WRSNs), a mobile charger (MC) moves around to compensate for sensor nodes’ energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm f...

This work addresses forecasting two essential factors in river hydrodynamics, which are discharge (Q) and water (H) levels. The accurate forecast of the two has long been a challenge in hydrological researches and flood prediction. While the traditional statistical models fail to capture the peak discharge during flooding seasons (i.e., due to the...

In the age of the ever-growing number of tasks being generated from IoT devices, one of the most crucial problems with enhancing the Quality of Service in multi-access computing is the system’s limited resources. To this end, Vehicular Fog Computing (VFC) has emerged as a potential solution that utilizes the idle resources of vehicles to reduce the...

The fifth generation of mobile wireless networks (5G) will provide an infrastructure with abundant and reliable connectivity for innovative and complicated applications. In 5G, 5G mobile devices, which will have improved computing resources for such applications, play an essential role. However, the network stack of 5G devices may continue to be bo...

A quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like region...

An incorrect version of Figure 7 appeared in our paper entitled “Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks” published in Wireless Networks.

Scheduling sensor activity to prolong the network lifetime while guaranteeing coverage and connectivity is a fundamental and critical issue in handling wireless sensor networks. Although many efforts have been made in this area, none of the prior works considers the relay hop bound constraint. As a result, the existing scheduling algorithms can pro...

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks’ lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and...