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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 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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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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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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Comparison of Conventional and Modified Ant Colony Approaches for Path Planning of Robot in an Indoor Environment 2

Comparison of Conventional and Modified Ant Colony Approache...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharan, Sudeep Ngoc Minh, Anh Tong Nguyen, Duc Thuan Dominguez-Jimenez, Juan Jose Nauth, Peter Frankfurt University of Applied Sciences Computer Science and Engineering Frankfurt am Main Germany Ucase Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

ISBN: (纸本)9798350371055

The optimal path for autonomous robots is highly needed, particularly in the industrial communities, manufacturing and social support industries. The meta-heuristic methods are strong and adaptable, and their employment in autonomous problems is taken into consideration. One of the methods used by the researchers is Ant Colony Optimization (ACO). However, this approach is less effective due to a few limitations. Thus, in order to mitigate its shortcomings and enhance the robustness of path planning, this paper aims to compare the different standard ACO algorithm with the new modified built algorithm, which is developed by taking the concept of ACO and extending it by condition-based rules, named as condition based-ant colony concept (CB-ACC), to solve path planning. The theoretical foundation of ACO with modification as another approach are used in this study to illustrate the ACO methods in the path planning, particularly in the static-complex environment. Simulation results show the comparison of the ACO approaches, including the Ant System (AS), the Ant Colony System (ACS) and the modified condition based-ant colony concept (CB-ACC), and how they are effective for static-complex environments compared to conventional ACO algorithms. Moreover, this paper presents the comparison results of the three approaches. © 2024 IEEE.

关键词： Ant Colony Optimization Ant Colony System Ant System Condition/rule-based System Modified ACO Path Planning

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LLMeLog: An Approach for Anomaly Detection based on LLM-enriched Log Events 35

LLMeLog: An Approach for Anomaly Detection based on LLM-enri...

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35th IEEE International Symposium on software Reliability engineering, ISSRE 2024

作者： He, Minghua Jia, Tong Duan, Chiming Cai, Huaqian Li, Ying Huang, Gang Peking University School of Software and Microelectronics Beijing China Peking University Institute for Artificial Intelligence Beijing China Peking University School of Computer Science Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350353884

Log-based anomaly detection is an essential task in maintaining software reliability. Existing log-based anomaly detection approaches often consist of three key phases: log parsing, event embedding, and model construction. Event embedding efficiently extracts semantic information from log events and produces vector representations of log events. However, existing event embedding methods suffer from two key problems. First, semantic noises are buried in log events leading to inevitable gaps between the obtained semantics from log events and their essential meanings. Second, there exists a gap between general semantic embedding and the specific embedding requirement of anomaly detection tasks. To mitigate these problems and improve the quality of representations of log events, we propose a novel anomaly detection approach named LLMeLog. It leverages the capabilities of large language models (LLMs) to enrich the contents of log events with in-context learning techniques. Then it utilizes the enriched log events to fine-Tune a pre-Trained BERT model. At last, it trains a transformer-based anomaly detection model with the event representations produced by the pre-Trained BERT model. Evaluation results on three public log datasets show that LLMeLog achieves the best performance across all datasets, boasting F1-scores exceeding 99%. Besides, when using only 10% of labeled data as training data, our approach can still achieve over 90% F1-scores. © 2024 IEEE.

关键词： Semantics

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Combining Autoencoder with Adaptive Differential Privacy for Federated Collaborative Filtering 28th

Combining Autoencoder with Adaptive Differential Privacy fo...

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

作者： Ding, Xuanang Li, Guohui Yuan, Ling Zhang, Lu Rong, Qian 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

Recommender systems provide users personalized services by collecting and analyzing interaction data, undermining user privacy to a certain extent. In federated recommender systems, users can train models on local devices without uploading raw data. Nevertheless, model updates transmitted between the user and the server are still vulnerable to privacy inference attacks. Several studies adopt differential privacy to obfuscate transmitted updates, but they ignore the privacy sensitivity of recommender model components. The problem is that components closer to the original data are more susceptible to privacy leakage. To address this point, we propose a novel adaptive privacy-preserving method combining autoencoder for federated collaborative filtering, which guarantees privacy meanwhile maintaining high model performance. First, we extend the variational autoencoder (VAE) to federated settings for privacy-preserving recommendations. Additionally, we analyze the privacy risks of the variational autoencoder model in federated collaborative filtering. Subsequently, we propose an adaptive differential privacy method to enhance user privacy further. The key is to allocate less privacy budget for sensitive layers. We apply a metric based on model weights to determine the privacy sensitivity of each layer in the autoencoder. Then we adaptively allocate the privacy budget to the corresponding model layer. Extensive experiments and analysis demonstrate that our method can achieve competitive performance to non-private recommender models meanwhile providing fine-grained privacy protection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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