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2020 International Conference on Information and Knowledge Management AnalytiCup, CIKM AnalytiCup 2020

作者： Piao, Guangyuan Huang, Weipeng Maynooth International Engineering College Department of Computer Science Maynooth University Maynooth Co Kildare Ireland Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland

In this report, we describe an ensemble approach with a set of enhanced random forest models for COVID-19 retweet prediction challenge at CIKM Analyticup 2020 held by the 29th ACM International Conference on Information and Knowledge Management. The proposed approach is based on a global model and a set of personalized models. The global model consists of a set of random forests enhanced by three different types of models such as linear regression, feed-forward neural networks, and factorization machines. In addition to this global model, we trained a number of personalized models for users that exist in both training and test sets and have a sufficient number of tweets for training. Our approach obtained a MSLE (Mean Squared Log Error) value of 0.149997 on the test set of the challenge and ranked 4th on the final leaderboard. © 2020 CEUR-WS. All rights reserved.

关键词： COVID-19

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Minimum Φ-distance estimators for finite mixing measures

arXiv

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arXiv 2023年

作者： Wei, Yun Mukherjee, Sayan Nguyen, XuanLong Department of Statistical Science Duke University NC United States Departments of Mathematics Computer Science Biostatistics & Bioinformatics Duke University NC United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany Department of Statistics University of Michigan Ann ArborMI United States

Finite mixture models have long been used across a variety of fields in engineering and sciences. Recently there has been a great deal of interest in quantifying the convergence behavior of the mixing measure, a fundamental object that encapsulates all unknown parameters in a mixture distribution. In this paper we propose a general framework for estimating the mixing measure arising in finite mixture models, which we term minimum Φ-distance estimators. We establish a general theory for the minimum Φ-distance estimator, where sharp probability bounds are obtained on the estimation error for the mixing measures in terms of the suprema of the associated empirical processes for a suitably chosen function class Φ. Our framework includes several existing and seemingly distinct estimation methods as special cases but also motivates new estimators. For instance, it extends the minimum Kolmogorov-Smirnov distance estimator to the multivariate setting, and it extends the method of moments to cover a broader family of probability kernels beyond the Gaussian. Moreover, it also includes methods that are applicable to complex (e.g., non-Euclidean) observation domains, using tools from reproducing kernel Hilbert spaces. It will be shown that under general conditions the methods achieve optimal rates of estimation under Wasserstein metrics in either minimax or pointwise sense of convergence;the latter case can be achieved when no upper bound on the finite number of components is *** Codes Primary 62H30, 62C20, Secondary 62F12 © 2023, CC BY-NC-ND.

关键词： Method of moments

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Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of data: Results from Clinical Neuroscience

arXiv

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arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

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Full wCDM analysis of KiDS-1000 weak lensing maps using deep learning

引用

Physical Review D 2022年第8期105卷 083518-083518页

作者： Janis Fluri Tomasz Kacprzak Aurelien Lucchi Aurel Schneider Alexandre Refregier Thomas Hofmann Institute of Particle Physics and Astrophysics Department of Physics ETH Zurich Wolfang-Pauli-Strasse 27 8093 Zurich Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Universitätstrasse 6 8006 Zurich Switzerland Swiss Data Science Center Paul Scherrer Institute Forschungsstrasse 111 5232 Villigen Switzerland Department of Mathematics and Computer Science University of Basel Spiegelgasse 1 4051 Basel Switherland Center for Theoretical Astrophysics and Cosmology Institute for Computational Science University of Zurich Winterthurerstrasse 190 8057 Zurich Switzerland

We present a full forward-modeled wCDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the cosmogrid, a novel massive simulation suite spanning six different cosmological parameters, we generate almost one million tomographic mock surveys on the sphere. Due to the large dataset size and survey area, we perform a spherical analysis while limiting our map resolution to HEALPix nside=512. We marginalize over systematics such as photometric redshift errors, multiplicative calibration and additive shear bias. Furthermore, we use a map-level implementation of the nonlinear intrinsic alignment model along with a novel treatment of baryonic feedback to incorporate additional astrophysical nuisance parameters. We also perform a spherical power spectrum analysis for comparison. The constraints of the cosmological parameters are generated using a likelihood-free inference method called Gaussian process approximate Bayesian computation (GPABC). Finally, we check that our pipeline is robust against choices of the simulation parameters. We find constraints on the degeneracy parameter of S8≡σ8ΩM/0.3=0.78−0.06+0.06 for our power spectrum analysis and S8=0.79−0.05+0.05 for our GCNN analysis, improving the former by 16%. This is consistent with earlier analyses of the 2-point function, albeit slightly higher. Baryonic corrections generally broaden the constraints on the degeneracy parameter by about 10%. These results offer great prospects for full machine learning based analyses of ongoing and future weak lensing surveys.

关键词： Cosmological parameters Large scale structure of the Universe Particle dark matter Numerical simulations in gravitation & astrophysics

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A security framework for 5G-enabled wireless sensor networks based on blockchain technology

A security framework for 5G-enabled wireless sensor networks...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： M S P Durgarao Dinesh Valluru S. Saraswathi P. Malathi K. Thilagam S. Loganathan Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Information Technology MLRITM Engineering CollegeHyderabad Dept of CSE - AIMLVel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of ECE Velammal Engineering College Chennai Tamil Nadu India Department of ECE Paavai Engineering College Namakkal Tamilnadu India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results.

关键词： Knowledge engineering Wireless sensor networks Scalability data integrity Network security Security Resource management

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The Value of Oxygenation Vital Signs in Machine Learning Prediction of Neurodevelopmental Outcomes in Preterm Infants

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Van Boven, Menne Bennis, Frank Onland, Wes Aarnoudse-Moens, Cornelieke Katz, Trixie Romijn, Michelle Hoogendoorn, Mark Leemhuis, Aleid Van Kaam, Anton Konigs, Marsh Oosterlaan, Jaap Amsterdam UMC Neonatal Department and Follow Me department The Emma Children's hospital and Amsterdam Reproduction and Development research institute Amsterdam Netherlands Amsterdam UMC Me department of the Emma Children's hospital Amsterdam Netherlands Vrije Universiteit Faculty of Science Department Computer Science Quantative Data Analytics Amsterdam Netherlands Amsterdam UMC Neonatal Department The Emma Children's hospital Amsterdam Netherlands Neonatal Department Amsterdam UMC Amsterdam Netherlands Amsterdam UMC Psychosocialc department The Emma Children's hospital Amsterdam Netherlands Amsterdam UMC Me department of the Emma Children's hospital Amsterdam Reproduction and Development research institute Amsterdam Netherlands

Machine learning models predicting neurodevelopmental outcome in preterm infants have great potential, but have often relied on inaccessible brain magnetic resonance imaging measurements. This study aimed to build models using readily available clinical predictors and investigate the potential of vital sign data in the prediction of neurodevelopmental outcome after preterm birth. Readily available predictors from the antenatal and neonatal period of preterm infants born © 2013 IEEE.

关键词： Prediction models

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Machine Learning Approaches for Advanced Threat Detection in Cyber Security

Machine Learning Approaches for Advanced Threat Detection in...

引用

Global Conference for Advancement in Technology (GCAT)

作者： V. Saravanan Talluri Upender E. D. Kanmani Ruby P. Deepalakshmi Desidi Narsimha Reddy Ananthi S N Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of CSE CMR College of Engineering and Technology. Medchal Hyderabad Telangana Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science Technology School of Computing Director - Accreditation and Ranking Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering S.A. Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machine learning (ML) algorithms for enhanced threat detection. This work intends to improve threat detection systems' accuracy, speed, and adaptability by utilizing ML algorithms' capabilities. The abstract begins by outlining the significance of machine learning in transforming traditional cybersecurity measures. It highlights the limitations of conventional signature-based methods, which often fail to detect novel and sophisticated threats. In contrast, ML-based approaches offer dynamic and proactive defenses capable of identifying previously unseen attack vectors. Furthermore, the paper delves into the practical implementation of ML models in real-world cybersecurity environments. It covers data collection and preprocessing techniques critical for training accurate and reliable models, as well as the challenges associated with maintaining and updating these models to adapt to evolving threat landscapes. The importance of feature selection and engineering in improving model performance is also emphasized. To validate the effectiveness of ML approaches, the study presents case studies and experimental results from various cybersecurity applications. The abstract concludes by addressing the future prospects and potential advancements in machine learning for cybersecurity. It discusses the role of emerging technologies, such as deep learning and federated learning, in enhancing threat detection capabilities. Additionally, it considers the ethical and privacy concerns associated with deploying ML models in cybersecurity and the need for robust governance frameworks. In summary, this study presents a thorough analysis of machine learning techniques for enhanced threat detection in cybersecurity, including useful applications, advantages, and difficulties.

关键词： Adaptation models Machine learning algorithms Accuracy Heuristic algorithms Machine learning data collection Threat assessment data models Vectors computer security

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Automation Through Machine Learning: Reinvigorating Cyber Security

Automation Through Machine Learning: Reinvigorating Cyber Se...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Jayashree V. Bagade Sukhvinder Singh Dari Rekha Dhivrani Santosh Chowhan G. Ravivarman V Janakiraman Department of Information Technology Vishwakarma Institute of Technology Pune india Symbiosis Law School Nagpur Symbiosis International Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats.

关键词： Automation Pandemics Prevention and mitigation Manuals Machine learning Turning Software Security Protection computer crime

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Cosmological Parameter Estimation and Inference using Deep Summaries

arXiv

引用

arXiv 2021年

作者： Fluri, Janis Kacprzak, Tomasz Refregier, Alexandre Lucchi, Aurelien Hofmann, Thomas Institute of Particle Physics and Astrophysics Department of Physics ETH Zurich Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Switzerland

The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this problem, we propose a novel approach to construct parameter estimators with a quantifiable bias using an order expansion of highly compressed deep summary statistics of the observed data. These summary statistics are learned automatically using an information maximising loss. Given an observation, we further show how one can use the constructed estimators to obtain approximate Bayes computation (ABC) posterior estimates and their corresponding uncertainties that can be used for parameter inference using Gaussian process regression even if the likelihood is not tractable. We validate our method with an application to the problem of cosmological parameter inference of weak lensing mass maps. We show in that case that the constructed estimators are unbiased and have an almost optimal variance, while the posterior distribution obtained with the Gaussian process regression is close to the true posterior and performs better or equally well than comparable methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

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Transforming Shopping with IoT: Exploring the Intelligent Way to Design the Smart Trolley using IoT Principles

Transforming Shopping with IoT: Exploring the Intelligent Wa...

引用

Big data & Machine Learning (ICBDML), IEEE International Conference on

作者： M. Jayaprakash A. Hency Juliet A. Jaya Mabel Rani N.T. Velusudha S. Lakshmisridevi V. Samuthira Pandi Department of Information Technology R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Data Analytics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

The interconnection of commonplace items is revolutionizing people's daily lives, thanks to the Internet of Things (IoT). A smart shopping system, consisting of interconnected products, might one day be a reality in supermarkets. In this Internet of Things system, products may be individually identified using low-cost RFID tags, which, when added to an RFID-enabled smart shopping cart, can be read automatically. This means shoppers won't have to endure the checkout line at all—billing can be handled right in the cart. The integration of RFID-equipped smart shelving into this system allows for the monitoring of inventory and, perhaps, the update of a central server. The suggested shopping system, which is dubbed IoT Assisted Smart Trolley (IoTAST), is cross-validated with the standard Smart Trolley (ST) model to clearly evaluate its performance. Since an RFID reader can automatically read all objects instead of a labourer physically scanning them, inventory management becomes considerably easier with this sort of technology. We construct a working system to test its functioning, determine the design criteria of a sophisticated purchasing system, and then create a reliable communication mechanism that renders the system feasible. This study validates the viability of the system. This is the first smart shopping system that takes security into account that we are aware of.

关键词： Protocols Prototypes Machine learning Reliability engineering Security Internet of Things Servers

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