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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Xiaojie Xia, Jing Gao, Shanshan Hao, Xingwei Zhou, Yuanfeng School of Software Shandong University Jinan China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350337488

The excellent inherent contrast between biological tissues afforded by MR imaging is one of the foremost characteristics of this technique, but it depends on the scan time and hardware devices. Recent studies have shown significant progress in deep learning-based single-image super-resolution (SISR) algorithms based on MR images. Many researchers have employed implicit functions for super-resolution tasks, achieving arbitrary resolution upsampling. Nevertheless, challenges like loss of generated image texture and weak high-frequency features persist. We propose an arbitrary multiple super-resolution model based on deep residual Fourier transform and a selfattention mechanism to tackle these issues. We first use the deep Fourier residual block to construct the high and low-frequency difference of the image, compensating for the spectral bias of the multiple perceptron (MLP) network. Building upon the substantial similarity in medical image tissue structures, we add a vertical and horizontal self-attention mechanism to capture the internal correlation of features in the vertical and horizontal directions. Finally, we learn a continuous functional representation to execute super-resolution tasks at any scale. Experiment results show the effectiveness of our method on the T2 sequence of public dataset SIMON and T1, T2, and Proton Density (PD) weighted scan sequences of public dataset IXI. We conduct qualitative and quantitative comparisons of the three contrasts, illustrating the superiority of our approach. © 2023 IEEE.

关键词： self-attention single-image super-resolution the deep Fourier residual block

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UAV Enabled Sustainable IoT Network with OTPDRL 26

UAV Enabled Sustainable IoT Network with OTPDRL

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Tiantian Liu, Lei Sun, Hongbo Hu, Jia Shandong University School of Software Jinan China YanTai University School of Computer and Control Engineering YanTai China Exeter University Computer Science United Kingdom

ISBN: (纸本)9798350331684

Integrating large-scale sensors into the network has become a research hotspot for its promising flexibility in monitoring vitally critical wild areas. However, the existing Internet of Things (IoT) systems are limited due to the lack of a stable power supply, which seriously affects the system's sustainability. The combination of sensors equipped with cordless power batteries and long-distance power transmission has ushered in a new era. Using the unmanned aerial vehicles (UAVs) to charge the battery ensures the flexibility and sustainability of the sensor in environmental detection. In this work, we aim to provide a solution for maintaining the sustainability of the sensors while optimizing UAV trajectory to minimize the overall energy consumption of UAV. Since deep reinforcement learning successfully solves the NP-hard combinatorial optimization problem, deep reinforcement learning is introduced in this work to obtain a feasible solution. We formulate the trajectory planning of UAV as a Markov decision problem and employ a deep reinforcement learning (DRL) model based on an attention mechanism to find the optimal policy efficiently, named the optimal trajectory planning algorithm based on DRL (OTPDRL). The experimental results suggest the OTPDRL obtains a good trade-off between performance gain and computational time. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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FedTop:a constraint-loosed federated learning aggregation method against poisoning attack

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Frontiers of computer science 2024年第5期18卷 233-235页

作者： Che WANG Zhenhao WU Jianbo GAO Jiashuo ZHANG Junjie XIA Feng GAO Zhi GUAN Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China China Unicom Beijing 100033China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].

关键词： constraint loose aggregation

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Adversarial Domain Adaptation for Non-Intrusive LOAD Monitoring 20

Adversarial Domain Adaptation for Non-Intrusive LOAD Monitor...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Bongyi, Gao Bei, Hui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Non-Intrusive Load Monitoring (NILM), known as energy disaggregation, is a method for determining the usage of separated devices by analyzing the overall energy consumption of an entire household. Understanding individual appliance energy consumption is crucial for customers to effectively manage usage and plan demand response programs. Recent advancements in data-driven methods have demonstrated promising performance in NILM. However, these methods rely on massive labeled data and would be dramatically degraded in novel environments. This paper presents ADAED, an energy disaggregation algorithm based on adversarial domain adaptation to monitor the loads of an unlabeled target domain by learning shared representations from a related source domain. Through the application of a sequence-to-point architecture and shared representations, ADAED can achieve more accurate predictions. Extensive experiments on three open benchmarks verify that our method can achieve better results than existing methods. © 2023 IEEE.

关键词： Energy utilization

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Attention Enhanced Feature Fusion for Mental Stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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A Concept Drift Tolerant Abnormal Detection Method for Network Traffic 15

A Concept Drift Tolerant Abnormal Detection Method for Netwo...

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15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Xu Han, Li Sun, Shimin Xu, Guowei Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology School of Computer Science and Engineering Tianjin China Tiangong University School of Software Tianjin China

ISBN: (纸本)9798350308693

we propose a cutting-edge solution that leverages passive adaptive methods based on ensemble learning to effectively detect anomalous traffic in data streams. Our approach tackles the issue of concept drift by integrating various deep learning models, each offering a unique perspective to learn data features. By doing so, our solution can accurately capture the distribution of data concepts and propose weight layers to make the model adaptable to changes caused by concept drift. In a series of experiments conducted on public datasets, our model demonstrated remarkable effectiveness in detecting anomalies, achieving accuracy rates of over 97% after adapting to concept drift. © 2023 IEEE.

关键词： Learning systems

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A Static Bi-dimensional Sample Selection for Federated Learning with Label Noise 28th

A Static Bi-dimensional Sample Selection for Federated Lear...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Rong, Qian Yuan, Ling Li, Guohui Li, Jianjun Zhang, Lu Ding, Xuanang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

In real-world Federated learning(FL), client training data may contain label noise, which can harm the generalization performance of the global model. Most existing noisy label learning methods rely on sample selection strategies that treat small-loss samples as correctly labeled ones, and large-loss samples as mislabeled ones. However, large-loss samples may also be valuable hard samples with correct labels. Also, these sample selection strategies have an issue in FL setting: Moreover sample selection strategies require training two models simultaneously and are executed in every mini-batch, which increases the communication cost and client computation. In this paper, we propose an efficient multi-stage federated learning framework to tackle label noise. Firstly, we use self-supervision pre-training to extract category-independent features without using any labels. Secondly, we propose a federated static two-dimensional sample selection(FedSTSS) method, which uses category probability entropy and loss as separation metrics to identify samples. Notably, to improve the accuracy of recognition samples, we add a weight entropy minimization term to the cross-entropy loss function. Finally, we use a semi-supervised method to finetune the global model on identified clean samples and mislabeled samples. Extensive experiments on multiple synthetic and real-world noisy datasets demonstrate that our method outperforms the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supervised learning

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GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data 24

GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labe...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhao, Haochen Meng, Hui Yang, Deqian Xie, Xiaozheng Wu, Xiaoze Li, Qingfeng Niu, Jianwei School of Computer Science and Engineering Beihang University Beijing China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China Institute of Software Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9798400706868

Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ *** state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled *** address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs.A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same *** this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated ***, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-the-art performance. © 2024 ACM.

关键词： Self-supervised learning

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