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Article ; Online: Internet of things for secure surveillance for sewage wastewater treatment systems.

Kumar, Priyan Malarvizhi / Hong, Choong Seon

2021 Volume 203, Page(s) 111899

Abstract: IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to ... ...

Abstract	IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to the water supply with minimal environmental effects or recovered directly. The major issue is monitoring the disposal of sewage in the treatment plants. Hence, this paper, Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, has been proposed for monitoring wastewater treatment and improving water quality. A smart water sensor enabled by IoT monitors water quality, water pressure, and water temperature and quantifies water dynamics to map water flow through the entire treatment facility. The proposed method calculates the wastewater treatment facility's effectiveness and ensures that chemical releases are maintained below allowable levels. Thus, the experimental results show the improved recycling water quality level is raised to 97.98%, enhancing secure communication and less moisture content when compared to other methods.
MeSH term(s)	Internet ; Internet of Things ; Sewage ; Water Purification ; Water Quality ; Water Supply
Chemical Substances	Sewage
Language	English
Publishing date	2021-08-17
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2021.111899
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Article: Internet of things for secure surveillance for sewage wastewater treatment systems

Kumar, Priyan Malarvizhi / Hong, Choong Seon

Environmental research. 2022 Jan., v. 203

2022

Abstract	IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to the water supply with minimal environmental effects or recovered directly. The major issue is monitoring the disposal of sewage in the treatment plants. Hence, this paper, Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, has been proposed for monitoring wastewater treatment and improving water quality. A smart water sensor enabled by IoT monitors water quality, water pressure, and water temperature and quantifies water dynamics to map water flow through the entire treatment facility. The proposed method calculates the wastewater treatment facility's effectiveness and ensures that chemical releases are maintained below allowable levels. Thus, the experimental results show the improved recycling water quality level is raised to 97.98%, enhancing secure communication and less moisture content when compared to other methods.
Keywords	Internet ; communications technology ; monitoring ; research ; sewage ; wastewater ; wastewater treatment ; water content ; water flow ; water quality ; water supply ; water temperature
Language	English
Dates of publication	2022-01
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2021.111899
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

Park, Yu Min / Tun, Yan Kyaw / Hong, Choong Seon

2023

Abstract: The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academic and industrial. However, to increase 6G/B5G network quality, conventional cellular networks that rely on ... ...

Abstract	The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academic and industrial. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the Amplitude and Phase-shift corresponding to reflection and transmission, which makes the existing terrestiral networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To tackle this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity. Comment: 8 pages, 9 figures, IEEE/IFIP Network Operations and Management Symposium (NOMS) 2024 submitted
Keywords	Computer Science - Information Theory ; Computer Science - Artificial Intelligence ; Computer Science - Networking and Internet Architecture
Subject code	003
Publishing date	2023-11-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Boosting Federated Learning Convergence with Prototype Regularization

Qiao, Yu / Le, Huy Q. / Hong, Choong Seon

2023

Abstract: As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data. However, the heterogeneous data distribution among clients often leads to ... ...

Abstract	As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data. However, the heterogeneous data distribution among clients often leads to a decrease in model performance. To tackle this issue, this paper introduces a prototype-based regularization strategy to address the heterogeneity in the data distribution. Specifically, the regularization process involves the server aggregating local prototypes from distributed clients to generate a global prototype, which is then sent back to the individual clients to guide their local training. The experimental results on MNIST and Fashion-MNIST show that our proposal achieves improvements of 3.3% and 8.9% in average test accuracy, respectively, compared to the most popular baseline FedAvg. Furthermore, our approach has a fast convergence rate in heterogeneous settings.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-07-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Contrastive encoder pre-training-based clustered federated learning for heterogeneous data.

Tun, Ye Lin / Nguyen, Minh N H / Thwal, Chu Myaet / Choi, Jinwoo / Hong, Choong Seon

Neural networks : the official journal of the International Neural Network Society

2023 Volume 165, Page(s) 689–704

Abstract: Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suffers from data heterogeneity problems, which can significantly affect its ... ...

Abstract	Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suffers from data heterogeneity problems, which can significantly affect its performance. To address this, clustered federated learning (CFL) has been proposed to construct personalized models for different client clusters. One effective client clustering strategy is to allow clients to choose their own local models from a model pool based on their performance. However, without pre-trained model parameters, such a strategy is prone to clustering failure, in which all clients choose the same model. Unfortunately, collecting a large amount of labeled data for pre-training can be costly and impractical in distributed environments. To overcome this challenge, we leverage self-supervised contrastive learning to exploit unlabeled data for the pre-training of FL systems. Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL. Leveraging these two crucial strategies, we propose contrastive pre-training-based clustered federated learning (CP-CFL) to improve the model convergence and overall performance of FL systems. In this work, we demonstrate the effectiveness of CP-CFL through extensive experiments in heterogeneous FL settings, and present various interesting observations.
MeSH term(s)	Humans ; Learning ; Cluster Analysis ; Privacy
Language	English
Publishing date	2023-06-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2023.06.010
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Article: When CVaR Meets With Bluetooth PAN: A Physical Distancing System for COVID-19 Proactive Safety.

Munir, Md Shirajum / Kim, Do Hyeon / Bairagi, Anupam Kumar / Hong, Choong Seon

IEEE sensors journal

2021 Volume 21, Issue 12, Page(s) 13858–13869

Abstract: In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing ... ...

Abstract	In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear model, and curve-fitting schemes. Notably, in the first stage, we determine a PAN creator's safe movement distance by proposing a probabilistic linear model. This scheme can effectively cope with a tail-risk from the probability distribution by satisfying the CVaR constraint for estimating safe movement distance. In the second stage, we design a Levenberg-Marquardt (LM)-based curve fitting algorithm upon the recommended safety distance and current distances between the PAN creator and others to find an optimal high-risk trajectory plan for the PAN creator. Finally, we have performed an extensive performance analysis using state-of-the-art Bluetooth data to establish the proposed risk-aware physical distancing system's effectiveness. Our experimental results show that the proposed solution approach can effectively reduce the risk of recommending safety distance towards ensuring private safety. In particular, for a 95% CVaR confidence, we can successfully deal with 45.11% of the risk for measuring the PAN creator's safe movement distance.
Language	English
Publishing date	2021-03-24
Publishing country	United States
Document type	Journal Article
ISSN	1530-437X
ISSN	1530-437X
DOI	10.1109/JSEN.2021.3068782
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning.

Thwal, Chu Myaet / Nguyen, Minh N H / Tun, Ye Lin / Kim, Seong Tae / Thai, My T / Hong, Choong Seon

Neural networks : the official journal of the International Neural Network Society

2023 Volume 170, Page(s) 635–649

Abstract: Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability to ... ...

Abstract	Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability to handle the unique challenges of distributed learning. While several variants of Vision Transformer (ViT) have shown great potential as alternatives to modern convolutional neural networks (CNNs) for centralized training, the unprecedented size and higher computational demands hinder their deployment on resource-constrained edge devices, challenging their widespread application in FL. Since client devices in FL typically have limited computing resources and communication bandwidth, models intended for such devices must strike a balance between model size, computational efficiency, and the ability to adapt to the diverse and non-IID data distributions encountered in FL. To address these challenges, we propose OnDev-LCT: Lightweight Convolutional Transformers for On-Device vision tasks with limited training data and resources. Our models incorporate image-specific inductive biases through the LCT tokenizer by leveraging efficient depthwise separable convolutions in residual linear bottleneck blocks to extract local features, while the multi-head self-attention (MHSA) mechanism in the LCT encoder implicitly facilitates capturing global representations of images. Extensive experiments on benchmark image datasets indicate that our models outperform existing lightweight vision models while having fewer parameters and lower computational demands, making them suitable for FL scenarios with data heterogeneity and communication bottlenecks.
MeSH term(s)	Humans ; Benchmarking ; Communication ; Machine Learning ; Neural Networks, Computer ; Privacy
Language	English
Publishing date	2023-11-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2023.11.044
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Self-Organizing Democratized Learning: Toward Large-Scale Distributed Learning Systems.

Nguyen, Minh N H / Pandey, Shashi Raj / Dang, Tri Nguyen / Huh, Eui-Nam / Tran, Nguyen H / Saad, Walid / Hong, Choong Seon

IEEE transactions on neural networks and learning systems

2023 Volume 34, Issue 12, Page(s) 10698–10710

Abstract: Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, ... ...

Abstract	Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that go beyond existing mechanisms such as federated learning (FL). Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this article. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn, is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.
Language	English
Publishing date	2023-11-30
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3170872
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Federated Learning based Energy Demand Prediction with Clustered Aggregation

Tun, Ye Lin / Thar, Kyi / Thwal, Chu Myaet / Hong, Choong Seon

2022

Abstract: To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. ... ...

Abstract	To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. Energy usage information collected by the clients' smart homes can be used to train a deep neural network to predict the future energy demand. Collecting data from a large number of distributed clients for centralized model training is expensive in terms of communication resources. To take advantage of distributed data in edge systems, centralized training can be replaced by federated learning where each client only needs to upload model updates produced by training on its local data. These model updates are aggregated into a single global model by the server. But since different clients can have different attributes, model updates can have diverse weights and as a result, it can take a long time for the aggregated global model to converge. To speed up the convergence process, we can apply clustering to group clients based on their properties and aggregate model updates from the same cluster together to produce a cluster specific global model. In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process. Comment: Accepted by BigComp 2021
Keywords	Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2022-10-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Robustness of SAM

Qiao, Yu / Zhang, Chaoning / Kang, Taegoo / Kim, Donghun / Zhang, Chenshuang / Hong, Choong Seon

Segment Anything Under Corruptions and Beyond

2023

Abstract: Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance of prompts. However, there is currently a lack of comprehensive evaluation ...

Abstract	Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance of prompts. However, there is currently a lack of comprehensive evaluation regarding its robustness under various corruptions. Understanding the robustness of SAM across different corruption scenarios is crucial for its real-world deployment. Prior works show that SAM is biased towards texture (style) rather than shape, motivated by which we start by investigating its robustness against style transfer, which is synthetic corruption. Following by interpreting the effects of synthetic corruption as style changes, we proceed to conduct a comprehensive evaluation for its robustness against 15 types of common corruption. These corruptions mainly fall into categories such as digital, noise, weather, and blur, and within each corruption category, we explore 5 severity levels to simulate real-world corruption scenarios. Beyond the corruptions, we further assess the robustness of SAM against local occlusion and local adversarial patch attacks. To the best of our knowledge, our work is the first of its kind to evaluate the robustness of SAM under style change, local occlusion, and local adversarial patch attacks. Given that patch attacks visible to human eyes are easily detectable, we further assess its robustness against global adversarial attacks that are imperceptible to human eyes. Overall, this work provides a comprehensive empirical study of the robustness of SAM, evaluating its performance under various corruptions and extending the assessment to critical aspects such as local occlusion, local adversarial patch attacks, and global adversarial attacks. These evaluations yield valuable insights into the practical applicability and effectiveness of SAM in addressing real-world challenges. Comment: The first work evaluates the robustness of SAM under various corruptions such as style transfer, local occlusion, and adversarial patch attack
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-06-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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