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International Conference on Science technology Engineering and Management (ICONSTEM)

作者： P. Sathyaraj Shankar Nayak Bhukya S. Rukmani Devi Chetan Umadi A. Ajina Rajendiran M Department of Electronics and Communication Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Computer Science Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Electronics& Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The fast spread of Internet of Things (IoT) gadgets has led to unprecedented number of interconnected systems offering many applications and services. Diverse elements in IoT networks increase security vulnerabilities, especially when adding new devices. IoT device onboarding secures device integration, authentication, and network connection authorization. This research shows the hazards and limitations of secure IoT device connections. Research examines onboarding protocols' merits and downsides. Bootstrapping, mutual authentication, and key establishment protect the IoT device-network connection in the proposed architecture. Overcoming scalability and resource constraints makes onboarding several IoT devices straightforward. We also analyze IoT network security with PKI and certificate-based authentication. Safe communication pathways and ongoing observation and system adjustments to address new security issues are discussed in this essay. Researchers test the technology in IoT environments. The framework handles onboarding in this case. The method safeguards onboarding and IoT ecosystem data, research demonstrate. A detailed device onboarding approach increases IoT security talks. The framework fixes onboarding issues and vulnerabilities for secure IoT networks.

关键词： Protocols Scalability Ecosystems Authentication Interconnected systems Network security Mathematics

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

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Topologically Faithful Multi-class Segmentation in Medical Images

arXiv

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arXiv 2024年

作者： Berger, Alexander H. Lux, Laurin Stucki, Nico Bürgin, Vincent Shit, Suprosanna Banaszak, Anna Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Quantitative Biomedicine University of Zurich Switzerland

Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-founded concepts from algebraic topology to binary segmentation. However, these approaches have been underexplored in multi-class segmentation scenarios, where topological errors are common. We propose a general loss function for topologically faithful multi-class segmentation extending the recent Betti matching concept, which is based on induced matchings of persistence barcodes. We project the N-class segmentation problem to N single-class segmentation tasks, which allows us to use 1-parameter persistent homology, making training of neural networks computationally feasible. We validate our method on a comprehensive set of four medical datasets with highly variant topological characteristics. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.7 © 2024, CC BY-NC-SA.

关键词： Image segmentation

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Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement

arXiv

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arXiv 2022年

作者： Weber, Leander Lapuschkin, Sebastian Binder, Alexander Samek, Wojciech Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin10587 Germany ICT Cluster Singapore Institute of Technology 138683 Singapore BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Informatics University of Oslo Oslo0373 Norway

Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of a multitude of methods to explain the decisions of black-box classifiers in recent years, these tools are seldomly used beyond visualization purposes. Only recently, researchers have started to employ explanations in practice to actually improve models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models, and systematically categorizes these approaches, comparing their respective strengths and weaknesses. We provide a theoretical perspective on these methods, and show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning, among others. We further discuss potential caveats and drawbacks of these methods. We conclude that while model improvement based on XAI can have significant beneficial effects even on complex and not easily quantifyable model properties, these methods need to be applied carefully, since their success can vary depending on a multitude of factors, such as the model and dataset used, or the employed explanation method. © 2022, CC BY-NC-SA.

关键词： Toys

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Analyzing the Effects of Different Urban and Rural Characteristics on Surface Urban Heat Island Intensity Using the Spatial Autoregressive Model

SSRN

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SSRN 2023年

作者： Wongsi, Noppachai Wongsai, Sangdao Wanishsakpong, Wandee Suwanprasit, Chanida Department of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathumthani12121 Thailand Thammasat University Research Unit in Data Learning Thammasat University Pathumthani12121 Thailand Department of Statistics Faculty of Science Kasetsart University Bangkok10900 Thailand Department of Geography Faculty of Social Sciences Chiang Mai University Chiang Mai50200 Thailand College of Arts Media and Technology Chiang Mai University Chiang Mai50200 Thailand

This study introduces an approach for quantifying the SUHII using the generalized mixed spatial autoregressive (SAR) model, in the Eastern Economic Corridor of Thailand. A comparative analysis was performed using the mean differences and simple linear regression (SLR), SLR with kernel density estimation (KDE), and spatial autoregressive regression (SAR) models. A year of MODIS LST was the dependent variable. The independent variables were the ISA percentage (%ISA), derived from the Sentinel-2 image using the RF classifier, and the geographical locations. Our findings indicated that using the mean differences on pixels without water surface and altitude led to the underestimated SUHII. By fitting the SLR with KDE on the smoothed %ISA, the LST was overfitted. The SLR applied to the original dataset mitigated the overestimation issue, but could not address the spatial dependency. The SAR model revealed a significant reduction in the spatial dependency and enhanced the model accuracy without overfitting. The 95% confidence intervals for LST from the SAR were found to be twice as narrow as those from other models. To evaluate the spatial assumption, we suggested making a map of how the spatial residuals are spread out, showing if there are clusters or dispersed. © 2023, The Authors. All rights reserved.

关键词： Atmospheric temperature

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OPTIMIZING EXPLANATIONS BY NETWORK CANONIZATION AND HYPERPARAMETER SEARCH

arXiv

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arXiv 2022年

作者： Pahde, Frederik Yolcu, Galip Ümit Binder, Alexander Samek, Wojciech Lapuschkin, Sebastian Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Germany Technische Universität Berlin Germany ICT Cluster Singapore Institute of Technology Singapore University of Oslo Norway BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures, including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effects of model canonization for various XAI methods in image classification tasks on the Pascal VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions

arXiv

引用

arXiv 2021年

作者： Hauer, Marc P. Kevekordes, Johannes Haeri, Maryam Amir TU Kaiserslautern Germany WWU Münster Germany TU Kaiserslautern Germany Learning Data-Analytics and Technology Department University of Twente Netherlands

With the increasing use of AI in algorithmic decision making (e.g. based on neural networks), the question arises how bias can be excluded or mitigated. There are some promising approaches, but many of them are based on a"fair" ground truth, others are based on a subjective goal to be reached, which leads to the usual problem of how to define and compute"fairness". The different functioning of algorithmic decision making in contrast to human decision making leads to a shift from a process-oriented to a result-oriented discrimination assessment. We argue that with such a shift society needs to determine which kind of fairness is the right one to choose for which certain scenario. To understand the implications of such a determination we explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions, analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective (based on EU law). © 2021, CC BY-NC-ND.

关键词： Decision making

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Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics

arXiv

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arXiv 2025年

作者： Alber, Maximilian Tietz, Stephan Dippel, Jonas Milbich, Timo Lesort, Timothée Korfiatis, Panos Krügener, Moritz Cancer, Beatriz Perez Shah, Neelay Möllers, Alexander Seegerer, Philipp Carpen-Amarie, Alexandra Standvoss, Kai Dernbach, Gabriel de Jong, Edwin Schallenberg, Simon Kunft, Andreas von Ankershoffen, Helmut Hoffer Schaeferle, Gavin Duffy, Patrick Redlon, Matt Jurmeister, Philipp Horst, David Ruff, Lukas Müller, Klaus-Robert Klauschen, Frederick Norgan, Andrew Aignostics Germany Department of Laboratory Medicine and Pathology Mayo Clinic RochesterMN United States Department of Radiology Mayo Clinic RochesterMN United States Department of Information Technology Mayo Clinic RochesterMN United States Systems Quality Office Mayo Clinic RochesterMN United States Machine Learning Group Technische Universität Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Department of Artificial Intelligence Korea University Korea Republic of Max-Planck Institute for Informatics Germany Berlin & Munich Partner Sites Germany Institute of Pathology Ludwig-Maximilians-Universität München Germany Institute of Pathology Charité – Universitätsmedizin Berlin Germany Germany

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charité - Universtätsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size. © 2025, CC BY-NC-ND.

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Deep learning for Uneven data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network

Deep Learning for Uneven Data in Industrial IoT Using a Dist...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Raj Kumar Gupta Naveena N B. Srinivasa Rao Rajasree RS Swagata Sarkar Kallakunta Ravi Kumar Physics Department Sardar Vallabhbhai Patel College Veer Kunwar Singh University Bhabua Ara Bihar Computer Technology-UG Kongu Engineering College Perundurai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Department of Artificial intelligence and Data Science Sri Sairam Engineering College Sai Leo Nagar Chennai 44 Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram AP

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques.

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From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis

arXiv

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arXiv 2025年

作者： Weers, Alexander Berger, Alexander H. Lux, Laurin Schüffler, Peter Rueckert, Daniel Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Germany Department of Computing Imperial College London United Kingdom Munich Center of Machine Learning Germany Munich Data Science Institute Technical University of Munich Munich Germany Institute of Pathology TUM School of Medicine and Health Technical University of Munich Munich Germany Weill Cornell Medicine Cornell University New York CityNY United States

The histopathological classification of whole-slide images (WSIs) is a fundamental task in digital pathology;yet it requires extensive time and expertise from specialists. While deep learning methods show promising results, they typically process WSIs by dividing them into artificial patches, which inherently prevents a network from learning from the entire image context, disregards natural tissue structures and compromises interpretability. Our method overcomes this limitation through a novel graph-based framework that constructs WSI graph representations. The WSI-graph efficiently captures essential histopathological information in a compact form. We build tissue representations (nodes) that follow biological boundaries rather than arbitrary patches all while providing interpretable features for explainability. Through adaptive graph coarsening guided by learned embeddings, we progressively merge regions while maintaining discriminative local features and enabling efficient global information exchange. In our method’s final step, we solve the diagnostic task through a graph attention network. We empirically demonstrate strong performance on multiple challenging tasks such as cancer stage classification and survival prediction, while also identifying predictive factors using Integrated Gradients. Our implementation is publicly available at https://***/HistoGraph31/pix2pathology © 2025, CC BY.

关键词： Classification (of information)

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