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Unsupervised Representation learning Method In Sensor Based Human Activity Recognition 21

Unsupervised Representation Learning Method In Sensor Based ...

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21st international conference on Machine learning and Cybernetics, ICMLC 2022

作者： Takenaka, Koki Hasegawa, Tatsuhito University of Fukui Graduate School for Engineering Japan

ISBN: (数字)9781665488327

ISBN: (纸本)9781665488327

Deep learning methods contribute to improve the estimation accuracy in human activity recognition (HAR) using sensor data. In general, the dataset used in HAR consists of accelerometer data and activity labels. Because of the widespread use of mobile devices, large amount of accelerometer sensor data without activity labels can be easily collected. The problem of annotation needs a large amount of time-consuming cost and human labor to annotate a activity labels to recorded sensor data. Therefore, we need a method to make deep learning models acquire feature representations from accelerometer data without activity labels in HAR. In this study, based on the unsupervised representation learning method proposed in image recognition, we proposed a new unsupervised representation learning method which combines segment discrimination (SD), autoencoder (AE) and feature independent softmax (FIS). Our experimental results showed that our proposed method outperformed the conventional method in fine-tuning accuracy in HAR. © 2022 IEEE.

关键词： Image recognition

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Gait Feature Processing for automated Human Gender Recognition 1st

Gait Feature Processing for Automated Human Gender Recogniti...

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1st international conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Sahu, Saumya Ranjan Bhagat, Surendra Nath Rout, Jyotiranjan Dash, Nilachakra Das, Tapas Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Odisha Balasore India Department of Information Technology Balasore College of Engineering and Technology Odisha Balasore India

ISBN: (纸本)9789811954023

The induction of technologies such as Internet of Everything (IoE) in every aspect of our day-to-day life has made life easier. The increasing use of computational decision-making in problem solving for scientific resolution of research problems is centered on such advanced algorithms that enhance the system performance results. The domain of biometric identification is also growing with great progress in IoT-based research and development. Other than the conventional biometric features, like the iris, fingertips, etc., different studies are done on the features of human movement activity to use it as a potential biometric marker. The proposed work has been done using a standard database collected using multimodal sensors which acquired data from Men as well Female volunteers. The experiments were undertaken in this work to show how feature reduction and methods for analysis of principle components, permit the modeling of a reliable, intelligent authentication system with the help of a conventional machine learning algorithm, for human gender identification, based on gait signature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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Optimal Option Hedging with Policy Gradient 21

Optimal Option Hedging with Policy Gradient

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Xiao, Bo Yao, Wuguannan Zhou, Xiang City Univ Hong Kong Dept Math Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Hong Kong Inst Data Sci Hong Kong Peoples R China

ISBN: (纸本)9781665424271

For the option hedging problem, we propose a reinforcement learning framework to statistically hedge generic financial options under the setting of non-zero risk-free interest rate and transaction cost. Instead of minimizing the risk of final wealth, we maximize the cumulative reward during the whole hedging process to find the optimal hedging strategies. In particular, we define the fundamental elements of reinforcement learning for the hedging problem. To illustrate the formulation, we utilize the Monte Carlo policy gradient with baseline onto both synthetic and realistic data to learn the state-value function and optimal policy function. Empirical evaluations demonstrate the effectiveness and performance of the proposed approach.

关键词： Option Hedging Reinforcement learning Policy Gradient

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Mental Health Prediction using Machine learning

Mental Health Prediction using Machine Learning

引用

international conference on ent Computing and Control Systems (ICICCS)

作者： Govindaraj M V Asha H Jayanth Harshitha D G Harshitha V Patil N. Mithili Devi Master of Computer Applications New Horizon College of Engineering Bengaluru India Master of Computer Application New Horizon College Of Engineering Bengaluru India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Major problems in the 21st century are mental health care problems. It can be considered as one of its important evils between men, and its source is in the largeness of awareness among the people. Using this paper, we would like to make a person realize that he is suffering from some kind of mental illness for instance, depression, anxiety, PTSD, insomnia, etc., by simply making the people aware of their symptoms using Machine learning. A sample of various ages, conditions, professions, sex, lifestyles taken from a group of respondents and collected with the help of a survey form containing questions which are often used by psychologists to understand the problem of their patient in detail has been applied machine learning algorithms on.

关键词： Surveys Technological innovation Machine learning algorithms Anxiety disorders Machine learning Mental health Predictive models Depression Socioeconomics Forecasting

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PBE-Plan: Periodic Backdoor Erasing Plan for Trustworthy Federated learning 25

PBE-Plan: Periodic Backdoor Erasing Plan for Trustworthy Fed...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Bei Li, Gaolei Chen, Mingzhe Liu, Yuchen Yi, Xiaoyu Li, Jianhua School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China University of Miami Electrical and Computer Engineering Department United States Nc State University Department of Computer Science United States

ISBN: (纸本)9798350330014

Backdoor attacks against Federated learning (FL) are highly disguised, persistent and mutable because the openness of participants increases the attacker's configurable space. Existing backdoor erasing strategies encounter numerous challenges in FL: 1) Erasing backdoor in a single round does not guarantee the trustworthiness of FL due to the unknown timing of incorporating poisoned samples into the training;2) Merely suppressing the backdoor response in the pre-softmax layer is insufficient to achieve stable erasing effects in complex FL tasks;3) Accuracy of the aggregated model deteriorates significantly without clean samples. In this paper, we propose a novel Periodic Backdoor Erasing Plan (PBE-Plan) for trustworthy FL, which enables the server to periodically erase the hidden backdoors in the global model without clean samples while minimizing the detriment of model accuracy. A task pipeline in PBE-Plan involves distilling a clean model from the backdoored global model and then distributing it to participants for local updates. The PBE-Plan uses data-free distillation to transfer knowledge without privacy infringement and prompts the student model's attention to the normal features of inputs while ignoring any potential backdoors by two-stage joint regularization (i.e., backdoor response suppression and attention map alignment). Experiments on CIFAR10 and CIFAR100 demonstrate that our approach can successfully reduce the attack success rate of 3 state-of-the-art backdoor attacks in FL from over 97% to less than 5 % with an accuracy loss of no more than 2 %. Meanwhile, our experiments also demonstrate that good defense results can be achieved without performing backdoor erasing at every round. © 2023 IEEE.

关键词： Distillation

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Graph Transfer learning 21

Graph Transfer Learning

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Gritsenko, Andrey Guo, Yuan Shayestehfard, Kimia Moharrer, Armin Dy, Jennifer Ioannidis, stratis Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

ISBN: (纸本)9781665423984

Graph embeddings have been tremendously successful at producing node representations that are discriminative for downstream tasks. In this paper, we study the problem of graph transfer learning: given two graphs and labels in the nodes of the first graph, we wish to predict the labels on the second graph. We propose a tractable, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy;in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random.

关键词： graph embedding transfer learning

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A Bibliometrics-Based Systematic Review on automated Essay Scoring in Education 7

A Bibliometrics-Based Systematic Review on Automated Essay S...

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7th international conference on Information Technology Systems and Innovation, ICITSI 2022

作者： Efendi, Toni Lubis, Fetty Fitriyanti Mutaqin Putri, Atina Waskita, Dana Sulistyaningtyas, Tri Rosmansyah, Yusep Sembiring, Jaka School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

Advances in information technology and computer science have brought significant changes to various sectors of human life. In education, computers have helped ease a variety of human tasks, especially for large-scale and repetitive tasks such as essay grading. Current's computer capabilities make it possible to do essay scoring automatically. Research related to automatic essay scoring continues to develop by trying certain techniques and methods to improve the accuracy of automatic scoring close to that of human scoring. This systematic review aims to explore the 21st century applications of automated essay grading used in education. We carried out a bibliometric analysis of the meta data obtained from the SCOPUS database using the Bibliometrix and VOSviewer software reporting the main themes of implementing automated essay scoring applications in educational studies, providing an overview of the current state of the art and future directions of research and applications. Based on bibliometric analysis, we found that research related to automatic essay assessment in education has a fluctuating development trend and the current direction of AES research has used deep learning techniques such as long short-term memory, transfer learning and bert to build a better AES. © 2022 IEEE.

关键词： Grading

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Role of Machine learning in Climate Change Prediction and Mitigation 2

Role of Machine Learning in Climate Change Prediction and Mi...

引用

2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action. © 2025 IEEE.

关键词： Global warming

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Mixture Gaussian Prototypes for Few-Shot learning 21

Mixture Gaussian Prototypes for Few-Shot Learning

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Jiang, Ruijin Cheng, Zhaohui Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China China Univ Geosci Sch Geophys & Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665424271

In this paper, we provide a new model named Mixture Gaussian Prototypes in few-shot classification problems. In order to describe their features more accurately, our mixture Gaussian prototypes use both the variance and the mean of the data in the support set, while the Gaussian distribution is concise so that it is suitable for the small sample task. Moreover, we verify the validity of our mixture Gaussian prototype through experiments. Also, we perform experiments on Omniglot, mini-ImageNet, and CUB for few-shot Classification and obtain high classification accuracy.

关键词： Few-shot learning Classification Problem Mixture Gaussian Prototypes

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Can Knowledge Transfer Techniques Compensate for the Limited Myocardial Infarction data by Leveraging Haemodynamics? An in silico study 21st

Can Knowledge Transfer Techniques Compensate for the Limited...

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21st international conference on Artificial Intelligence in Medicine (AIME)

作者： Tenderini, Riccardo Betti, Federico Senouf, Ortal Yona Muller, Olivier Deparis, Simone Buffa, Annalisa Abbe, Emmanuel Ecole Polytech Fed Lausanne EPFL Inst Math CH-1015 Lausanne Switzerland Lausanne Univ Hosp CHUV Dept Cardiol CH-1011 Lausanne Switzerland Ecole Polytech Fed Lausanne EPFL Sch Comp & Commun Sci CH-1015 Lausanne Switzerland

ISBN: (纸本)9783031343438;9783031343445

The goal of this work is to investigate the ability of transfer learning (TL) and multitask learning (MTL) algorithms to predict tasks related to myocardial infarction (MI) in a small-data regime, leveraging a larger dataset of haemodynamic targets. The data are generated in silico, by solving steady-state Navier-stokes equations in a patient-specific bifurcation geometry. stenoses, whose location, shape, and dimension vary among the datapoints, are artificially incorporated in the geometry to replicate coronary artery disease conditions. The model input consists of a pair of greyscale images, obtained by postprocessing the velocity field resulting from the numerical simulations. The output is a synthetic MI risk index, designed as a function of various geometrical and haemodynamic parameters, such as the diameter stenosis and the wall shear stress (WSS) at the plaque throat. Moreover, the Fractional Flow Reserve (FFR) at each outlet branch is computed. The ResNet18 model trained on all the available MI labels is taken as reference. We consider two scenarios. In the first one, we assume that only a fraction of MI labels is available. For TL, models pretrained on FFR data - learned on the full dataset - reach accuracies comparable to the reference. In the second scenario, instead, we suppose also the number of known FFR labels to be small. We employ MTL algorithms in order to leverage domain-specific feature sharing, and significant accuracy gains with respect to the baseline single-task learning approach are observed. Ultimately, we conclude that exploiting representations learned from haemodynamics-related tasks improves the predictive capability of the models.

关键词： Myocardial infarction Fractional flow reserve Haemodynamics Knowledge transfer Transfer learning Multitask learning Coronary angiography

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