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Error Density-dependent Empirical Risk Minimization

Expert Systems with Applications 2024年 254卷

作者： Chen, Hong Zhang, Xuelin Gong, Tieliang Gu, Bin Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen518000 China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518000 China School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Artificial Intelligence Jilin University Jilin130012 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Empirical Risk Minimization (ERM) with the squared loss has become one of the most popular principles for designing learning algorithms. However, the existing mean regression models under ERM usually suffer from poor generalization performance due to their sensitivity to atypical observations (e.g., outliers). For alleviating this sensitivity, some strategies have been proposed by utilizing the quantitative relationship of error values with different observations to form robust learning objectives. Instead of focusing on error values, this paper considers the error density to uncover the structure information of observations and proposes a new learning objective, called Error Density-dependent Empirical Risk Minimization (EDERM), for robust regression under complex data environments. Property characterizations and experimental analysis validate the robustness and competitiveness of the proposed EDERM-based learning models. The implemented codes can be found at https://***/zhangxuelincode/EDERM. © 2024 Elsevier Ltd

关键词： Learning algorithms

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023 50

Predicting Neurological Recovery from Coma After Cardiac Arr...

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50th Computing in Cardiology, CinC 2023

作者： Reyna, Matthew A Amorim, Edilberto Sameni, Reza Weigle, James Elola, Andoni Rad, Ali Bahrami Seyedi, Salman Kwon, Hyeokhyen Zheng, Wei-Long Ghassemi, Mohammad M Van Putten, Michel Jam Hofmeijer, Jeannette Gaspard, Nicolas Sivaraju, Adithya Herman, Susan T Lee, Jong Woo Westover, M Brandon Clifford, Gari D Emory University Department of Biomedical Informatics United States Weill Institute for Neurosciences University of California Department of Neurology San Francisco United States Massachusetts General Hospital Department of Neurology United States University of the Basque Country UPV/EHU Department of Electronic Technology Spain Shanghai Jiao Tong University Department of Computer Science and Engineering China Michigan State University Department of Computer Science and Engineering United States University of Twente Clinical Neurophysiology Group Netherlands Medisch Spectrum Twente Department of Neurology and Clinical Neurophysiology Netherlands Rijnstate Hospital Department of Neurology Netherlands Yale School of Medicine Department of Neurology United States Hôpital Universitaire de Bruxelles and Université Libre de Bruxelles Service de Neurologie Belgium Barrow Neurological Institute Comprehensive Epilepsy Center Department of Neurology United States Brigham and Women's Hospital Department of Neurology United States Beth Israel Deaconess Medical Center Department of Neurology United States Massachusetts General Hospital McCance Center for Brain Health United States Harvard Medical School United States Georgia Institute of Technology Department of Biomedical Engineering United States

ISBN: (纸本)9798350382525

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest research consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide. © 2023 CinC.

关键词： Electroencephalography

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Augmented Selection of Cluster Heads and Enlightening Network Lifetime in Underwater Wireless Sensor Networks Using Grey Wolf Optimization (GWO) 6

Augmented Selection of Cluster Heads and Enlightening Networ...

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6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： Hole, Snehal Zaboun, Abdul Razzaq T. Al-Ghanimi, Hayder Hussen, Omad Aldeen Alchilibi, Haider Abdul Kadeem, Sahar R. S.N.G Institute of Management and Research Pune India Al-Esraa University College Forensic Sciences Department Baghdad Iraq Techniques Engineering Hilla University College Department of Medical Instrumentation Babylon Iraq Al-Kunooze University College Computer Engineer Department Basra Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad10022 Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350304480

The Underwater Wireless Sensor Network (UWSN) is a collection of sensor nodes deployed beneath the seabed to monitor and record challenging applications in offshore exploration and oceanic environments. In order to ensure proper energy consumption in a dynamic environment, the effective forwarding of data packets through efficient paths is crucial. Routing, therefore, plays a significant role in reducing power consumption by making informed decisions on packet destinations. In this study, we propose and analyze an efficient routing technique for underwater wireless sensor networks based on Grey-Wolf Optimization. The proposed technique is evaluated under various conditions, and the experimental results demonstrate a reduced number of iterations for selecting a cluster head, leading to a longer network lifetime compared to existing protocols such as Energy Efficient Routing Protocol (EERP) and Directional Flooding based Routing (DFR). © 2023 IEEE.

关键词： DFR EERP Energy routing Grey wolf lifetime

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Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System 27

Optimizing Forest Fire Prevention: Intelligent Scheduling Al...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Ecole Supérieure des Sciences Economiques et Commerciales de Tunis Université de Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： algorithms drone fire Forest monitoring

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DISENTANGLING THE PROSODY AND SEMANTIC INFORMATION WITH PRE-TRAINED MODEL FOR IN-CONTEXT LEARNING BASED ZERO-SHOT VOICE CONVERSION

arXiv

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arXiv 2024年

作者： Chen, Zhengyang Wang, Shuai Zhang, Mingyang Liu, Xuechen Yamagishi, Junichi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Chinese University of Hong Kong Shenzhen China National Institute of Informatics Tokyo Japan

Voice conversion (VC) aims to modify the speaker's timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens, facilitating disentanglement of speech content information. Recently, in-context learning (ICL) has emerged in text-to-speech (TTS) systems for effectively modeling specific characteristics such as timbre through context conditioning. This paper proposes an ICL capability enhanced VC system (ICL-VC) employing a mask and reconstruction training strategy based on flow-matching generative models. Augmented with semantic tokens, our experiments on the LibriTTS dataset demonstrate that ICL-VC improves speaker similarity. Additionally, we find that k-means is a versatile tokenization method applicable to various pre-trained models. However, the ICL-VC system faces challenges in preserving the prosody of the source speech. To mitigate this issue, we propose incorporating prosody embeddings extracted from a pre-trained emotion recognition model into our system. Integration of prosody embeddings notably enhances the system's capability to preserve source speech prosody, as validated on the Emotional Speech Database. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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ALGNN: Auto-Designed Lightweight Graph Neural Network 18th

ALGNN: Auto-Designed Lightweight Graph Neural Network

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Cai, Rongshen Tao, Qian Tang, Yufei Shi, Min School of Software South China University of Technology Guangzhou China Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton United States Institute for Informatics Washington University School of Medicine in St. Louis St. Louis United States

ISBN: (纸本)9783030891879

Graph neural networks (GNNs) are widely used on graph-structured data, and its research has made substantial progress in recent years. However, given the various number of choices and combinations of components such as aggregator and activation function, designing GNNs for specific tasks is very heavy manual work. Recently, neural architecture search (NAS) was proposed with the aim of automating the GNN design process and generating task-dependent architectures. While existing approaches have achieved competitive performance, they are not well suited to practical application scenarios where the computational budget is limited. In this paper, we propose an auto-designed lightweight graph neural network (ALGNN) method to automatically design lightweight, task-dependent GNN architectures. ALGNN uses multi-objective optimization to optimize the architecture constrained by the computation cost and complexity of the model. We define, for the first time, an evaluation standard for consumption cost with the analysis of the message passing process in GNNs. Experiments on real-world datasets demonstrate that ALGNN can generate a lightweight GNN model that has much fewer parameters and GPU hours, meanwhile has comparable performance with state-of-the-art approaches. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Mutual information based logistic regression for phishing URL detection

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Ronin Institute Montclair NJ United States Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

Phishing is a cybersecurity problem that hackers employ to deceive individuals and organizations. Phishing is dynamic in nature;the hackers change several tricks to deceive the victims in multiple ways. It is important to track the tricks of hackers with recent technology. This study makes a notable contribution to enhancing cybersecurity defences by offering insights that aid in the detection and mitigation of phishing threats. Specifically, the study's analysis of URLs using mutual information and logistic regression techniques yielded a remarkably high accuracy rate of 99.97%, surpassing previous efforts. The identification of the most informative features for distinguishing phishing attempts provides valuable intelligence for cybersecurity professionals, enabling them to bolster defenses and stay ahead of evolving phishing tactics. © 2024

关键词： Feature selection Logistic regression Mutual information Phishing URL analysis

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Overview of Energy Harvesting and Sustainability in Smart Cities

Overview of Energy Harvesting and Sustainability in Smart Ci...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ramiz Salama Chadi Altrjman Fadi Al-Turjman Satya Prakash Yadav Satvik Vats Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT. Near East University Near East Boulevard Mersin 10 Turkiye Department of Chemical Engineering Waterloo University ON Canada Artificial Intelligence Engineering Dept AI and Robotics Institute Near East University Nicosia Mersin10 Turkey Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and A1anagement(GLBITA1) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Sustainability is a key factor in the context of smart cities. In smart cities, promoting sustainable energy is one strategy for achieving sustainability and lowering greenhouse gas emissions. Different energy sources are needed to meet the need for affordable and dependable electrical energy, where the environmental impact typically outweighs the cost of generation. In order to increase sustainability in the electrical industry, smart cities are examined in this article. The methodological strategy for this involved conducting a systematic literature review utilizing the PRISMA procedure. 154 journal articles were examined in-depth for this review. Energy efficiency, renewable energy, and energy and urban planning were the themes that the results were organized into. The results of the study showed that: (a) there is a global academic publication landscape for research on smart cities and energy sustainability; (b) publications are unbalanced when comparing the results of studies on the energy use intensity of different continents to those on the energy sustain ability of smart cities; and (c) there is a strong focus on technology-related issues related to energy sustainability, efficiency, and renewables topics in the literature, but much less attention is given to these issues. Urban and energy authorities are informed by the insights produced, and scholars are given ideas for future research directions.

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MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels

arXiv

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arXiv 2024年

作者： Xu, Moucheng Zhou, Yukun Goodwin-Allcock, Tobias Firoozabadi, Kimia Jacob, Joseph Alexander, Daniel C. Slator, Paddy J. UCL Hawkes Institute United Kingdom UCL Satsuma Lab United Kingdom UCL Respiratory United Kingdom UCL Department of Computer Science United Kingdom UCL Medical School United Kingdom UCL Department of Medical Physics and Biomedical Engineering United Kingdom Cardiff University School of Computer Science and Informatics United Kingdom Cardiff University Brain Research Imaging Center United Kingdom

We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps that strongly relate to underlying microstructure. Quantitative maps are calculated by fitting a model to multiple images, e.g. with least-squares or machine learning. However, the overwhelming majority of model fitting techniques assume that each voxel is independent, ignoring any co-dependencies in the data. This makes model fitting sensitive to voxelwise measurement noise, hampering reliability and repeatability. We propose a self-supervised deep variational approach that breaks the assumption of independent pixels, leveraging redundancies in the data to effectively perform data-driven regularisation of quantitative maps. We demonstrate that our approach outperforms current model fitting techniques in dMRI simulations and real data. Especially with a Gaussian mixture prior, our model enables sharper quantitative maps, revealing finer anatomical details that are not presented in the baselines. Our approach can hence support the clinical adoption of parameter mapping methods such as dMRI and qMRI. Our code is available at https://***/moucheng2017/MRI-GMM-VAE. © 2024, CC BY-NC-ND.

关键词： Mapping

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