Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In contrast, CT scanners are more widely available and significantly less expensive. In response to these challenges, our study addresses the issue of generating PET images from CT images, aiming to reduce both the medical examination cost and the associated health risks for patients. Our contributions are twofold: First, we introduce a conditional diffusion model named CPDM, which, to our knowledge, is one of the initial attempts to employ a diffusion model for translating from CT to PET images. Second, we provide the largest CT-PET dataset to date, comprising 2,028,628 paired CT-PET images, which facilitates the training and evaluation of CT-to-PET translation models. For the CPDM model, we incorporate domain knowledge to develop two conditional maps: the Attention map and the Attenuation map. The former helps the diffusion process focus on areas of interest, while the latter improves PET data correction and ensures accurate diagnostic information. Experimental evaluations across various benchmarks demonstrate that CPDM surpasses existing methods in generating high-quality PET images in terms of multiple metrics. The source code and data samples are available at https://github.com/thanhhff/CPDM.

Federated Learning (FL) has emerged as an effective paradigm allowing multiple parties to collaboratively train a global model while protecting their private data. However, it is observed that the performance of FL approaches tends to degrade significantly when data are sparsely distributed across clients with small datasets. This is referred to as the sparse-and-scarce challenge, where data held by each client is both sparse (does not contain examples to all classes) and scarce (small dataset). Sparse-and-scarce data diminishes the generalizability of clients’ data, leading to intensive over-fitting and massive domain shifts in the local models and, ultimately, decreasing the aggregated model's performance. Interestingly, while this scenario is a specific manifestation of the well-known non-IID11This refers to the generic situation where local data distributions are not identical and independently distributed.challenge in FL, it has not been distinctly addressed. Our empirical investigation highlights that generic approaches to the non-IID challenge often prove inadequate in mitigating the sparse-and-scarce issue. To bridge this gap, we develop SAFA, a novel FL algorithm that specifically addresses the sparse-and-scarce challenge via a novel continual model iteration procedure. SAFA maximally exposes local models to the inter-client diversity of data with minimal effects of catastrophic forgetting. Our experiments show that SAFA outperforms existing FL solutions, up to 17.86%, compared to the prominent baseline. The code is accessible viahttps://github.com/HungNguyen20/SAFA.

Federated Learning has emerged as a decentralized method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while keeping their data stored locally. However, a significant challenge arises when dealing with missing modalities in clients’ datasets, where certain features or modalities are unavailable or incomplete, leading to heterogeneous data distribution. Previous studies have addressed this issue, but they fall short in addressing generalizability across diverse, unobserved individuals. This study introduces MIFL, a novel framework for handling modality-missing clients in Multimodal Federated Learning. MIFL utilizes a unique approach, optimizing a client’s local model with existing modalities while incorporating absent modalities from clients. Aggregation of these models is performed through a graph-based attentive aggregation method, maintaining generalized characteristics by updating a global model averaged across clients. Our experimental results demonstrate the effectiveness of MIFL across various client configurations with statistical heterogeneity, showcasing its potential for addressing the challenge of missing modalities in Federated Learning.

Federated Learning has emerged as a transformative paradigm in the realm of collaborative machine learning, enabling the training of global models across decentralized devices without the need for centralizing data. While Federated Learning has shown remarkable promise, a critical limitation lies in its ability to personalize models to individual clients. Current approaches predominantly emphasize improving the accuracy of trained clients, inadvertently sidelining the significance of accommodating unseen clients. Furthermore, most of the existing personalized federated learning approaches require new clients to provide labeled data and undergo extensive retraining, posing a substantial barrier and hindering the broader adoption and engagement of potential users within these systems.In this paper, we introduce a novel and comprehensive solution to address these challenges: a generalized method for Personalized Federated Learning. Our approach transcends the limitations of conventional Federated Learning techniques by not only optimizing the accuracy of trained clients but also ensuring exceptional performance among unseen clients, even in diverse settings. Throughout extensive experiments, our method demonstrates significant improvements concerning the performance of seen and unseen clients, respectively, while eliminating the need for labeled data and model re-training among unseen clients.

Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e., subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent subgraph mining (FSM), as maximal frequent subgraphs lie in the middle of graph lattice, and FSM algorithms must explore an exponential space and an NP-hard subroutine of frequency counting. Different from prior research, which primarily focused on optimal solutions, we introduce pmMine, a progressive graph neural framework designed for MFSM in a single large graph to attain an approximate solution. The framework combines isomorphic graph embedding, non-parametric partitioning, and an efficiently top-down pattern searching strategy. The critical insight that makes pmMine work is to define the concepts of rooted subgraph and isomorphic graph embedding, in which the costly isomorphism subroutine can be efficiently performed using similarity estimation in embedding space. In addition, pmMine returns the patterns identified during the mining process in a progressive manner. We validate the efficiency and effectiveness of our technique through extensive experiments on a variety of datasets spanning various domains.

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 identically distributed. In this paper, we tackle a new type of Non-IID data, called cluster-skewed non-IID, discovered in actual data sets. The cluster-skewed non-IID is a phenomenon in which clients can be grouped into clusters with similar data distributions. By performing an in-depth analysis of the behavior of a classification model's penultimate layer, we introduce a metric that quantifies the similarity between two clients' data distributions without violating their privacy. We then propose an aggregation scheme that guarantees equality between clusters. In addition, we offer a novel local training regularization based on the knowledge-distillation technique that reduces the overfitting problem at clients and dramatically boosts the training scheme's performance. We theoretically prove the superiority of the proposed aggregation over the benchmark FedAvg. Extensive experimental results on both standard public datasets and our in-house real-world dataset demonstrate that the proposed approach improves accuracy by up to 16% compared to the FedAvg algorithm.

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, raising questions about the shortfall in current defenses' robustness guarantees. Specifically, most existing defenses cannot eliminate edge-case backdoor attacks or suffer from a trade-off between backdoor-defending effectiveness and overall performance on the primary task. To tackle this challenge, we propose FedGrad, a novel backdoor-resistant defense for FL that is resistant to cutting-edge backdoor attacks, including the edge-case attack, and performs effectively under heterogeneous client data and a large number of compromised clients. FedGrad is designed as a two-layer filtering mechanism that thoroughly analyzes the ultimate layer's gradient to identify suspicious local updates and remove them from the aggregation process. We evaluate FedGrad under different attack scenarios and show that it significantly outperforms state-of-the-art defense mechanisms. Notably, FedGrad can almost 100% correctly detect the malicious participants, thus providing a significant reduction in the backdoor effect (e.g., backdoor accuracy is less than 8%) while not reducing main accuracy on the primary task.

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 non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client’s impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.

Target coverage and connectivity are two of the most crucial issues in handling wireless sensor networks. However, maintaining these two factors is challenging due to the energy constraint of sensors. To this end, wireless charging has emerged as a promising solution to prolong the sensor's lifetime. In a wireless charging sensor network, a mobile charger moves around the network, stops at several charging locations and charges the sensor via electromagnetic waves. In this study, we investigate the problem of optimizing the charging location and charging time of the mobile charger to ensure the target coverage and connectivity of the network. Our main idea is to leverage the Deep Reinforcement Learning approach. Specifically, the mobile charger will act as an agent, which receives a state including the energy information of the sensors. The mobile charger then decides the following charging location and charging time using the state information and the knowledge learned in the past. Experimental results have shown that our algorithm can extend the network lifetime (i.e., the time until the network coverage and connectivity are not guaranteed) up to 245.9 times compared to the existing algorithms.

In this paper, we address vehicle-based mobile crowdsensing for air quality monitoring applications. We tackle a novel issue that asks to determine monitoring frequencies for maximizing spatial-temporal coverage while reducing the monitoring costs and balancing load across the vehicles. We begin by theoretically formulating the problem and proposing an objective function that considers the three goals. We then leverage the evolutionary approach to develop an algorithm for determining the optimal monitoring frequency. We conduct comprehensive experiments to evaluate the performance of the proposed approach and compare it to the other methods. The results indicate that our approach can enhance the objective function by a factor of 1.33 to 4 compared to the others.

Multi-access edge computing (MEC) is seen as an effective technique for decreasing service latency in a V2X network by offloading computational activities. With MEC, a three-tier offloading architecture can be developed, where a vehicle can offload computational tasks to a cloud by communicating with a base station (gNB) and Road Side Units (RSUs). In this paper, we focus on three-tier V2X networks which rely on three offloading paths: Vehicle-to-Infrastructure (i.e., vehicle to RSU), Vehicle-to-Cloud (i.e., vehicle to gNB), and Infrastructure-to-Cloud (i.e., RSU to gNB). We propose an offloading strategy based on deep reinforcement learning with the goal of reducing the average latency of tasks. To be more specific, we leverage the Deep Q Network to estimate the goodness of action-state value to determine the offloading decision. We also propose a novel exploration scheme and a new model training strategy. The experimental findings indicate that our proposed offloading method outperforms the state-of-the-art, particularly in critical circumstances characterized by a high rate of vehicle arrival or packet generation.

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 the road via Wi-Fi. We tackle a new problem that is how to reduce the cost of 4G while preserving the latency. We propose an offloading strategy that combines a reinforcement learning technique known as Q-learning with Fuzzy logic to accomplish the purpose. Q-learning assists devices in learning to decide the communication channel. Meanwhile, Fuzzy logic is used to optimize the reward function in Q-learning. The experiment results show that our offloading method significantly cuts down around 30-40% of the 4G communication cost while keeping the latency of 99% packets below the required threshold.

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 subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.

Air pollution is a critical issue in cities in developing countries like Hanoi, Vietnam. An efficient and comprehensive air quality monitoring system may reduce the harmfulness and improve the cities' sustainability. This paper presents a novel approach to realize such a system in which the air monitoring sensors are mobile. More specifically, we introduce a three-tier architecture for the air quality system, including sensing, communication, and application layers. Initially, we discuss each layer concept to bypass the limitation of the traditional stationary monitoring system. We then describe our design and implementation of air quality monitoring devices installed on vehicles, such as buses. The device is carefully designed to satisfy the conditions of impedance matching and power integrity. Besides, it fully functions in measuring parameters from the ambient environment. The device is aware of its location (using GPS) and uses Wi-Fi and 4G (LTE) to transmit sensing data on the Internet. We have conducted various experiments, including a trial deployment of the devices on a vehicle running in Hanoi. The results show our device achieves sensing data transmission with high-reliability levels (i.e., 97%, 100% on Wi-Fi, 4G (LTE), respectively). Moreover, the trial deployment confirms the feasible operation of our device in actual condition.

In a three-tier Vehicle to X (V2X) network, a vehicle can offload the computational tasks to the edge computing component at a roadside unit (RSU) or a base station with cloud computing (gNB). Moreover, an RSU can also offload to gNB, forming three offloading paths: vehicle-to-RSU, vehicle-to-gNB, and RSU-to-gNB. This paper aims to minimize the offloaded tasks' average latency while dealing with the network dynamic. Note that the existing works assume the fixed network parameters, hence have failed to address the dynamic. As a solution, we use the multi-agent multi-armed bandits (MBA) learning for offloading that can adapt to the network dynamic and optimize the latency. More importantly, we propose a new MBA offloading scheme with an exploration mechanism based on the Sigmoid function. We conduct an extensive evaluation to evaluate and show the superiority of our proposal. First, the proposed Sigmoid exploration mechanism reduces the tasks' average latency by 35% compared to a basic MBA using negative rewarding. Second, the simulation results show our proposed offloading algorithm shortens the task latency by 18.5% on average and 56.9% in the best case, compared to the state-of-the-art.

We focus on real-time air quality monitoring systems that rely on devices installed on automobiles in this research. We investigate an opportunistic communication model in which devices can send the measured data directly to the air quality server through a 4G communication channel or via Wi-Fi to adjacent devices or the so-called Road Side Units deployed along the road. We aim to reduce 4G costs while assuring data latency, where the data latency is defined as the amount of time it takes for data to reach the server. We propose an offloading scheme that leverages Q-learning to accomplish the purpose. The experiment results show that our offloading method significantly cuts down around 40-50% of the 4G communication cost while keeping the latency of 99.5% packets smaller than the required threshold.

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 of the existing approaches mainly leverage recurrence or convolution-based neural networks. However, while recurrent neural networks (RNNs) usually suffer from slow training speed due to sequential computation and encounter problems as vanishing gradient or bottleneck, CNN endures a trade-off between complexity and performance. In this paper, we introduce SAFL, a self-attention-based neural network model with the focal loss for scene text recognition, to overcome the limitation of the existing approaches. The use of focal loss instead of negative log-likelihood helps the model focus more on low-frequency samples training. Moreover, to deal with the distortions and irregular texts, we exploit Spatial TransformerNetwork (STN) to rectify text before passing to the recognition network. We perform experiments to compare the performance of the proposed model with seven benchmarks. The numerical results show that our model achieves the best performance.

This paper introduces a novel charging strategy for wireless rechargeable sensor networks (WRSNs), in which a mobile charger (MC) moves and wirelessly transfers the power to the sensor nodes. The first distinct point of this work is designing the MC's charging algorithm under the consideration of target coverage and connectivity. As a solution, we introduce a novel on-demand charging scheme for WRSNs that optimize the charging time at each MC's charging location. Moreover, we take advantage of the Q-learning technique (i.e., hence named our algorithms Q-charging) to maximize the number of monitored targets. Q-charging can prioritize the sensor nodes, which play a more critical role in the network. Hence, Q-charging can select a suitable charging location aiming to provide sufficient power for the prioritized sensors. We have evaluated our proposal in comparison to the previous works. The evaluation results show that Q-charging can prolong the time until the first target is not monitored by 5.2 times on the average, and 14.3 times in the best case, compared to existing algorithms.

There has been observed explosive growth in the development of mobile applications (apps) for Android and iOS operating systems, which has led to the direct impact towards mobile app development. In order to design and propose quality-oriented apps, it is the primary responsibility of developers to devote time and sufficient efforts towards testing to make the apps bug-free and operational in the hands of end-users without any hiccup. Manual testing procedures take a prolonged amount of time in writing test cases, and in some cases, the full testing requirements are not met. Besides, the insufficient knowledge of tester also impacts the overall quality and bug-free apps. To overcome the obstacles of testing, we propose a new testing methodology cum tool called “AgileUATM” which works primarily towards white-box and black-box testing. To evaluate the validity of the proposed tool, we put the tool in a real-time operational environment concerning mobile test apps. By using this tool, all the acceptance criteria are determined via user stories. The testers/developers specify requirements with formal specifications based on programs properties, predicates, invariants, and constraints. The results show that the proposed tool generated effective and accurate test cases, test input. Meanwhile, expected output was also generated in a unified fashion from the user stories to meet acceptance criteria. The proposed solution also reduced the development time to identify test data as compared to manual Behavior-Driven Development (BDD) methodologies. This tool can support the developers to get a better idea about the required tests and able to translate the customer’s natural languages to computer languages as well. This paper fulfills an approach to suitably test mobile application development.

Sensor scheduling for maximizing the network lifetime and achieving the full area coverage is a paramount problem in wireless sensor networks. Although considerable effort has been devoted, this problem is still a challenge to the research community. The approximation algorithms proposed so far couldn't guarantee the performance ratio. In this paper, we first formulate the problem under linear programming model which can help to determine the exact optimal solution. Then, in order to reduce the time complexity, we propose a (1 +∊)-approximation algorithm based on divide-and-conquer technique. The main idea is to divide the network into sub-regions, then determine the suboptimal solution for every sub-region and combine them to obtain the total solution of the whole network. Moreover, with the aim of speeding up the suboptimal solution finding process, we propose an approximation algorithm using the column generation approach. The experiment results show the superiority of our proposed algorithms over the existing ones.

Geographic routing is one of the most popular routing protocols in wireless sensor networks (WSNs) due to its simplicity and efficiency. However, with the occurrence of holes, geographic routing incurs with the so-called local minimum problem that may lead to a long hole detour path as well as the traffic concentration around the hole boundary. In consequence, the network lifetime is shortened. In this paper, we aim at proposing a lightweight distributed geographic routing protocol, which can prolong the lifetime of WSNs under the hole occurrence. Our main idea is to exploiting Q-learning technique to estimate the distance from a node to the holes. The routing decision is then determined based on the residual energy of the nodes, their estimated distance to the holes, and their distance to the destination. The simulation experiments show that our protocol strongly outperforms state-of-the-art protocols in terms of the network lifetime, packet latency and energy consumption. Specifically, our proposed protocol extends the network lifetime by more than 12% compared to the existing protocols.

Because of its simplicity and scalability, geographic routing is a popular approach in wireless sensor networks, which can achieve a near-optimal routing path in the networks without holes (i.e., regions without working sensors). With the occurrence of holes, however, geographic routing faces the problems of load imbalance and routing path enlargement. In the literature, several proposals have attempted to fix these issues, but the majority of them considers only the cases when both the source and the destination stay fairly far from the holes. Recently, a few work has been proposed to tackle the problem of routing in the vicinity of routing holes. However, none of them addresses the two problems (i.e., load imbalance and routing path enlargement) concurrently, and none of them can solve the problem of load imbalance thoroughly. In this paper, we introduce a novel approach in dealing with routing in the vicinity of holes, that is the first to target and solve both the load imbalance and path enlargement problems. The theoretical analysis proves that the routing path stretch of our proposed protocol can be controlled to be as small as 1 + ε (for any predefined ε> 0) and the simulation experiments show that our protocol strongly outperforms the existing protocols in terms of load balancing.

Wireless sensor networks (WSNs) are used in many mission-critical applications, such as target tracking on a battlefield, emergency alarms, and disaster detection. In such applications, QoS provisioning in the timeliness domain is indispensable. Moreover, because of the diversity of sensory data, QoS provisioning should support not only one but multiple levels of end-to-end delay constraints. As a result of several characteristics such as the limitations on the energy supply, available storage and computational capacity of the sensor nodes, guaranteeing timely delivery in WSNs is a challenging problem. To overcome these limitations, several lightweight and stateless QoS-based geographic routing protocols have been proposed. The existing protocols work well in networks without routing holes (i.e., regions with no working sensors). However, with the occurrence of routing holes, they suffer from the so-called local minimum phenomenon and traffic congestion around the hole boundary. In this paper, we consider the presence of routing holes and propose a delay-guaranteed geographic routing protocol called DEHA that can support multiple end-to-end delay levels. The main idea is to achieve early awareness of the presence of a routing hole and then to utilize this awareness in determining a routing path that can avoid the hole. Simulation results show that our protocol outperforms the existing protocols in terms of several performance metrics, including packet delivery ratio, energy efficiency, and load balancing.

The occurrence of multiple holes in wireless sensor networks poses many challenges in designing routing protocols. The traditional scheme is forwarding packets along the hole perimeters. However, this scheme leads to two serious problems: data concentration around the hole boundaries and routing path enlargement Recently, several approaches have been proposed to address these two problems, wherein a common idea is to form forbidden areas around the holes from which packets are kept to stay away. However, due to the static nature of the forbidden areas and routing paths, the existing protocols cannot solve these two problems thoroughly. In this paper, we propose a novel protocol for bypassing multiple holes in wireless sensor networks which can balance the traffic over the network while ensuring the constant stretch property of the routing path. Our main idea is to use elastic forbidden areas and dynamic routing paths. The theoretical analysis proves that the routing path stretch of the proposed protocol can be controlled to be as small as 1 + ϵ (for any predefined ϵ > 0), and the simulation experiments show that our protocol strongly outperforms state-of-the-art protocols in terms of load balancing.

IDF is an Incremental Development Framework which supports the development and the verification of UML models for reactive systems.IDF offers refinement and extension techniques allowing liveness properties to be preserved during the model developments.Here, we improve the framework in order to analyze models from a safety point of view.For this purpose, we associate IDF with the experienced tools of safety analysis based on the BIP language by translating UML models into BIP.We demonstrate on a basic example the complementarity of liveness and safety analyses.

Concurrent systems are prone to deadlocks that arise from competing access to shared resources and synchronization between the components. At the same time, concurrency leads to a dramatic increase of the possible state space due to interleavings of computations, which makes standard verification techniques often infeasible. Previous work has shown that approximating the state space of component based systems by computing invariants allows to verify much larger systems then standard methods that compute the exact state space. The approach comes with the drawback, though, that not all of the reported specification violations may be reachable in the system. This paper deals with that problem by combining the information from the invariant with model checking techniques and strategies for reducing the memory footprint. The approach is implemented as post processing step for generating the exact set of reachable specification violations along with traces to demonstrate the error.

We consider a compositional method for the verification of component-based systems described in a subset of the BIP language encompassing multi-party interactions. The method is based on the use of two kinds of invariants. Component invariants are over-approximations of components' reach ability sets. Interaction invariants are constraints on the states of components involved in interactions. In this paper we propose fixed point characterization for computing interaction invariants. We also propose a new technique that takes the incremental design of the system into account. In many situations, the technique will help to avoid redoing all the verification process each time an interaction is added in the design. Our two techniques have been implemented as extension of the D-Finder toolset. The result has been applied to check deadlock-freedom on several case studies. Our experiments show that our new methodology is generally much faster than existing ones.

Autonomous robots are complex systems that require the interaction/cooperation of numerous heterogeneous software components. Nowadays, robots are critical systems and must meet safety properties including in particular temporal and real-time constraints. We present a methodology for modeling and analyzing a robotic system using the BIP component framework integrated with an existing framework and architecture, the LAAS Architecture for Autonomous System, based on Geno