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Weisfeiler Leman for Euclidean Equivariant machine learning 41

Weisfeiler Leman for Euclidean Equivariant Machine Learning

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41st international conference on machine learning, ICML 2024

作者： Hordan, Snir Amir, Tal Dym, Nadav Faculty of Mathematics Technion - Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion - Israel Institute of Technology Haifa Israel

The k-Weisfeiler-Leman (k-WL) graph isomorphism test hierarchy is a common method for assessing the expressive power of graph neural networks (GNNs). Recently, GNNs whose expressive power is equivalent to the 2-WL test were proven to be universal on weighted graphs which encode 3D point cloud data, yet this result is limited to invariant continuous functions on point clouds. In this paper, we extend this result in three ways: Firstly, we show that PPGN (Maron et al., 2019a) can simulate 2-WL uniformly on all point clouds with low complexity. Secondly, we show that 2-WL tests can be extended to point clouds which include both positions and velocities, a scenario often encountered in applications. Finally, we provide a general framework for proving equivariant universality and leverage it to prove that a simple modification of this invariant PPGN architecture can be used to obtain a universal equivariant architecture that can approximate all continuous equivariant functions uniformly. Building on our results, we develop our WeLNet architecture, which sets new state-of-the-art results on the N-Body dynamics task and the GEOM-QM9 molecular conformation generation task. Copyright 2024 by the author(s)

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Ensemble Models for Multi-class Classification of Diabetic Retinopathy 9th

Ensemble Models for Multi-class Classification of Diabetic R...

引用

9th international conference on Pattern Recognition and machine Intelligence (PReMI)

作者： Sahayam, Subin Manasa, Tutturu Lakshmi Jayaraman, Umarani Indian Inst Informat Technol Design & Mfg Chennai 600127 Tamil Nadu India

ISBN: (纸本)9783031126994;9783031127007

Artificial intelligence-based methods help in the automatic detection and diagnosis of various diseases in the medical field. Diabetes affects 5 in 10 people in the world. It can cause diabetic retinopathy(DR) that damages the retina, leading to irreversible vision loss. DR is generally diagnosed by an Ophthalmologist using a patient's fundus image. The Ophthalmologist grades the disease based on severity. Early intervention can delay the progression of the disease. Automatic disease detection can help large-scale patient screening, early detection of diabetic retinopathy, and reduce human error. The work aims to study various ensemble learning models and their ensemble voting methods for the DR classification task. The study also focuses on the effects of data augmentation along with preprocessing. The performance of each of the models has been studied using the Aptos-blindness detection 2019 dataset.

关键词： Multi-class Classification Microaneurysm Transfer learning Ensemble learning

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Reserve Price Optimization in First -Price Auctions via Multi -Task learning 23

Reserve Price Optimization in First -Price Auctions via Mult...

引用

23rd IEEE international conference on data Mining (IEEE ICDM)

作者： Kalra, Achir Wang, Chong Borcea, Cristian Chen, Yi Forbes Media LLC New York NY 07310 USA Amazon Ads New York NY USA New Jersey Inst Technol Newark NJ USA

ISBN: (纸本)9798350307887

Online publishers typically sell ad impressions through auctions held in ad exchanges in real-time, i.e., real-time bidding (RTB). A publisher will accept the winning bid if it is higher than a given reserve price for an ad impression. Setting an appropriate reserve price for an ad impression is critical for publishers' revenue generation, but also challenging. While this problem has been studied for second-price auctions, it lacks studies for first-price auctions, the de facto industry standard since 2019. This paper proposes a machine learning model that determines the optimal reserve prices for individual ad impressions in real-time. It uses a multi-task learning framework to predict the lower bounds of the highest bids with a coverage probability, using only the data available to publishers. The experiments using data from a large international publisher show that the proposed model outperforms the comparison systems on generating revenue.

关键词： computational advertising neural networks prediction interval estimation survival analysis proportional hazards model

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FL-CLIP: BRIDGING PLASTICITY AND STABILITY IN PRE-TRAINED FEDERATED CLASS-INCREMENTAL learning MODELS

FL-CLIP: BRIDGING PLASTICITY AND STABILITY IN PRE-TRAINED FE...

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Tan, Alysa Ziying Feng, Siwei Yu, Han Nanyang Technol Univ NTU Coll Comp & Data Sci Singapore Singapore Nanyang Technol Univ Alibaba NTU Singapore Joint Res Inst Singapore Singapore Alibaba Grp Hangzhou Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Federated learning (FL) is the prevailing paradigm in privacypreserving machine learning. Despite recent advances yielding state-of-the-art outcomes, FL systems face challenges in adapting to dynamic real-world scenarios, where the local data distributions of clients may shift over time. This limitation stems from the common assumption of data stationarity in existing FL methods. In this paper, we propose the Federated learning framework for CLass Incremental Pretrained models (FL-CLIP) to bridge this gap, by enabling continuous adaptation to changes in underlying FL client data distributions. The focus is on the incremental adaptation of existing FL models, trained on a substantial number of base classes, to newly arriving classes. To address the issue of catastrophic forgetting, the concepts of plasticity and stability are integrated into two distinct stages of FL-CLIP. Additionally, a task-weighted auxiliary loss is designed to tackle the class imbalance problem, and we propose a lightweight distillation method to enable FL-CLIP to adapt to resourceconstrained environments. Extensive experiments demonstrate that FL-CLIP significantly outperforms 7 state-of-theart baselines in terms of average task accuracy, achieving a balanced performance across base and novel classes with a smaller memory footprint.

关键词： Federated learning Continual learning Class-Incremental learning Catastrophic Forgetting

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A Review of Deep learning Techniques for Human Activity Recognition 22nd

A Review of Deep Learning Techniques for Human Activity Reco...

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22nd international conference on Hybrid Intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Dhattarwal, Aayush Ratnoo, Saroj Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar125001 India

ISBN: (纸本)9783031274084

In recent years, the research in Human Activity Recognition (HAR) has grown manifold due to the easy availability of data and its important role in many real-world applications. Since the performance of classical machine learning algorithms is not up to the mark, the focus is on applying deep learning algorithms for enhancing the efficacy of HAR systems. This review includes the research works carried out during the period of 2019–2022 in three recognition domains-human activity, surveillance systems and sign language. This review considers the methodologies applied, dataset used and the major findings and achievements of these recent HAR studies. Finally, the paper points out the various challenges in the field of Activity Recognition requiring further attention from researchers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Detection Syn Flood and UDP Lag Attacks Based on machine learning Using AdaBoost

Detection Syn Flood and UDP Lag Attacks Based on Machine Lea...

引用

2023 international conference on data science and Its Applications, ICoDSA 2023

作者： Syafiuddin, Nova Hanafi Mandala, Satria Cahyani, Niken Dwi Wahyu Telkom University Informatics Bandung Indonesia

ISBN: (纸本)9798350305197

Syn flood is a commonly used Distributed Denial-of-Service (DDoS) attack that aims to overwhelm a server by sending a large number of Transmission Control Protocol (TCP) SYN requests without completing the handshake process and rejecting user packets. On the other hand, UDP flood attacks target the network infrastructure rather than the server, making it difficult to identify the source of the attack. In recent years, research related using machine learning to detect Distributed Denial-of-Service (DDoS) attacks with different methods. Steps taken to detect SYN Flood and UDP Lag attacks are system design, data collection, search, and data analysis. Test metrics such as precision, recall, accuracy, and F1-score are closely related to machine learning algorithms used for detecting Distributed Denial-of-Service (DDoS) attacks, including SYN Flood and UDP Lag attacks. Some literatures on SYN Flood attack detection have a low accuracy value with algorithm has been used. With the research conducted by author in detecting attacks by designing and building a system using machine learning algorithm which include ensemble learning using AdaBoost and CICDDoS2019 dataset. For AdaBoost is an ensemble algorithm boosting type classifier that each individual model has its own way self to build sequentially by repeating the previous and CICDDoS2019 dataset was created by the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick. © 2023 IEEE.

关键词： Denial-of-service attack

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Multi-machine learning Approach for Mapping Drought-Driven Fires in Kalimantan Using Geo-AI data

Multi-Machine Learning Approach for Mapping Drought-Driven F...

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2024 international conference on Aerospace Electronics and Remote Sensing Technology

作者： Sofan, Parwati Nugroho, Gatot Suwarsono, Suwarsono Widodo, Joko Setyaningrum, Nugraheni Cahyana, Destika Awaluddin, Awaluddin Yulihastin, Erma Qodrita, Lely Risyanto, Risyanto Natl Res & Innovat Agcy BRIN Res Ctr Geoinformat Bandung Indonesia Natl Res & Innovat Agcy BRIN Res Ctr Food Crops Cibinong Indonesia Natl Res & Innovat Agcy BRIN Res Ctr Climate & Atmosphere Bandung Indonesia

ISBN: (纸本)9798331542023;9798331542016

This paper presents a comparative analysis of four machine learning algorithms-Random Forest (RF), Gradient Boosting (GTB), Support Vector machine (SVM), and Classification and Regression Trees (CART)-for mapping drought-driven forest and land fires in Kalimantan. We utilized multi-source Geo-Artificial Intelligence data, including satellite remote sensing products and spatial data, incorporating physical, environmental, climatological, biological, and socio-economic (anthropogenic) factors as inputs. The El Nino year of 2019 was selected as the study period for data analysis. The Global Annual Burned Area Maps (GABAM) product was refined using nighttime active fire data, which represent actual fire occurrences and served as our reference map. We applied the Graph Regularization fusion method to all the classified images to enhance mapping accuracy. Our results indicate that RF and GTB significantly outperformed SVM and CART in terms of precision, recall, Kappa, and overall accuracy, with both RF and GTB achieving an overall accuracy of 97%. The fused image yielded results similar to those of the RF and GTB images. These findings offer valuable insights for developing a national early warning system for drought-related forest and land fires, using Geo-AI data to support forest management and the goal of achieving net-zero emissions.

关键词： Geo-Artificial Intelligence (Geo-AI) Drought-driven forest/land fires Kalimantan machine learning

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Comparing the Effectiveness of machine learning Algorithm Implementations Based on the Use of Cloud Services 17

Comparing the Effectiveness of Machine Learning Algorithm Im...

引用

17th international conference on Engineering of Modern Electric Systems, EMES 2023

作者： Moisi, Elisa Valentina Coman, Simina Maria Gabor, Gianina Pater, Alexandrina Mirela Coman, Ovidiu Popescu, Daniela-Elena University of Oradea Computer Science and Information Technology Department Oradea Romania

ISBN: (纸本)9798350310634

Considering the problem of diabetes prediction for the use case of PIMA Indian data Set and Diabetes dataset 2019, the present paper compares the efficiency of two automatic machine learning tools without code offered by Microsoft and Matlab: Automated ML from Azure machine learning Studio, and the Classification Learner Application from Matlab. Also, the results obtained for the best model obtained using the automatic machine tools without code, are compared with those obtained for an implementation with code, made on-premise. © 2023 IEEE.

关键词： learning algorithms

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Toward Automated Foot Type Diagnosis: A Deep learning Approach with Attention Mechanism and Grad-CAM Interpretability 11

Toward Automated Foot Type Diagnosis: A Deep Learning Approa...

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11th IEEE international conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE international conference on Robotics, Automation and Mechatronics (RAM)

作者： Wu, Sihan Wang, Yubing Lin, Ze Xie, Longhan South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou Peoples R China

ISBN: (纸本)9798350364200;9798350364194

Plantar pressure distribution plays a critical role in assessing foot function, diagnosing foot disorders, and designing orthotics. Traditional machine learning methods have limitations in capturing complex spatial patterns and achieving high accuracy due to reliance on manual feature selection. These methods often rely on manually selected features and struggle to extract intricate relationships within the plantar pressure data, leading to suboptimal performance. Recently, hierarchical features can be automatically observed from raw data by mechine learning approaches. These methods can capture both local and global patterns, demonstra superior performance in extracting intricate features and delivery robust classification results. Nevertheless, their inherent complexity often leads to a lack of interpretability. In this paper, an application of deep learning method for plantar pressure pattern classification is introduced, which integrates the ResNet residual network architecture and the Convolutional Block Attention Module (CBAM). To enhance interpretability, we employ Grad-CAM visualizations to highlight salient regions contributing to the model's predictions, providing insights into the learned features and guiding feature selection for traditional classification methods. Experimental results show that our method achieves a high accuracy of 98.5% on a comprehensive plantar pressure dataset. The approach, proves to be reliable and effective for categorizing plantar pressure distribution patterns, holding promise for clinical applications in foot assessment and treatment.

关键词： Plantar pressure distribution Deep learning ResNet residual network Convolutional Block Attention Module Gradient-weighted Class Activation Mapping

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machine learning Assisted Soft Error Variation Analysis of Radiation Hardened MRAM Circuit 23

Machine Learning Assisted Soft Error Variation Analysis of R...

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IEEE 23rd international conference on Nanotechnology (NANO)

作者： Shukla, Alok Kumar Nehete, Hemkant Kaushik, Partha Soni, Sandeep Kaushik, Brajesh Kumar Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee Uttarakhand India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttarakhand India

ISBN: (纸本)9798350333466

Radiation-induced soft errors are becoming more challenging to the reliability of conventional memories with the downscaling process technology and increasing miniaturization of transistors. Spintronic-based memory is a viable contender for addressing this challenge due to its non-volatility, high speed, and radiation tolerance. However, radiation induced charges can still affect hybrid spintronic/CMOS peripheral circuitry. machine learning (ML) can be a powerful approach for accelerating the analysis of radiation-hardened circuits, especially in the context of magnetic random-access memory (MRAM) technology. This paper proposes a novel machine learning (ML)-assisted approach for investigating the variability of radiation induced charges on radiation hardened MRAM circuit to shorten the loop of technology pathfinding. Our approach is based on training ML based algorithms to predict the probability of soft errors occurring in the radiation hardened (RH)-MRAM circuit. ML algorithms are trained using a combination of simulated data, allowing it to accurately model the effects of radiation-induced disturbances on the circuit. Our ML assisted approach provided a more accurate and efficient analysis of soft errors in the MRAM circuit, with R2 scores of 0.99. Furthermore, significant improvement in prediction time is observed as compared to the SPICE simulation time.

关键词： machine learning (ML) radiation effects radiation-hardened single event upset (SEU) soft error

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