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Machine Learning Algorithms for the Detection of Threats in IoT Healthcare 1

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Machine Learning Algorithms for the Detection of Threats in ...

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Arun, V. Shenbagavalli, P. Sridhar, T. Manivannan, B. Mahesh, T.R. Anitha, K. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India Karunya Institute of Technology and Sciences Division of Digital Sciences India Alliance University Alliance College of Engineering and Design Bangalore India Computer Science and Engineering Namakkal India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350359756

IoT (Internet of Things) technology enables the connectivity and communication of healthcare-related equipment, medical devices, and sensors. This connectivity enables better monitoring, data collecting, and analysis, which leads to better patient outcomes and more effective healthcare delivery. However, with the rising use of linked devices, it is critical to detect and prevent Cyberattacks in order to secure sensitive patient information. In order to tackle this problem, the article delves into the application of machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB), These algorithms were evaluated using the WUSTL EHMS2020 dataset and demonstrated the highest level of accuracy. © 2023 IEEE.

关键词： Internet of things

Inscribing or Circumscribing a Histogon to a Convex Polygon 42

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Inscribing or Circumscribing a Histogon to a Convex Polygon

42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2022

作者： Chung, Jaehoon Bae, Sang Won Shin, Chan-Su Yoon, Sang Duk Ahn, Hee-Kap Department of Computer Science and Engineering Pohang University of Science and Technology Korea Republic of Division of Computer Science and Engineering Kyonggi University Suwon Korea Republic of Division of Computer Engineering Hankuk University of Foreign Studies Seoul Korea Republic of Department of Service and Design Engineering SungShin Women's University Seoul Korea Republic of Graduate School of Artificial Intelligence Department of Computer Science and Engineering Pohang University of Science and Technology Korea Republic of

ISBN: (纸本)9783959772617

We consider two optimization problems of approximating a convex polygon, one by a largest inscribed histogon and the other by a smallest circumscribed histogon. An axis-aligned histogon is an axis-aligned rectilinear polygon such that every horizontal edge has an integer length. A histogon of orientation θ is a copy of an axis-aligned histogon rotated by θ in counterclockwise direction. The goal is to find a largest inscribed histogon and a smallest circumscribed histogon over all orientations in [0, π). Depending on whether the horizontal width of a histogon is predetermined or not, we consider several different versions of the problem and present exact algorithms. These optimization problems belong to shape analysis, classification, and simplification, and they have applications in various cost-optimization problems. © Jaehoon Chung, Sang Won Bae, Chan-Su Shin, Sang Duk Yoon, and Hee-Kap Ahn;licensed under Creative Commons License CC-BY 4.0.

关键词： Optimization

Approximating Convex Polygons by Histogons* 34

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Approximating Convex Polygons by Histogons*

34th Canadian Conference on Computational Geometry, CCCG 2022

作者： Chung, Jaehoon Bae, Sang Won Shin, Chan-Su Yoon, Sang Duk Ahn, Hee-Kap Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of Division of Computer Science and Engineering Kyonggi University Suwon Korea Republic of Division of Computer Engineering Hankuk University of Foreign Studies Seoul Korea Republic of Department of Service and Design Engineering SungShin Women’s University Seoul Korea Republic of Graduate School of Artificial Intelligence Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

We study the problem of finding the largest inscribed histogon and the smallest circumscribed histogon for a convex polygon. A histogon is an axis-aligned rectilinear polygon such that every horizontal edge has an integer length. Depending on whether the horizontal width of a histogon is predetermined or not, we consider four different versions of the problem and present exact algorithms. © 2022 Yeganeh Bahoo and Konstantinos Georgiou.

关键词： Geometry

Towards Understanding and Applying Security Assurance Cases for Automotive Systems

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arXiv 2024年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology Gothenburg University Gothenburg Sweden

Security Assurance Cases (SAC) are structured bodies of arguments and evidence used to reason about security properties of a certain artefact. SAC are gaining focus in the automotive domain as the need for security assurance is growing due to software becoming a main part of vehicles. Market demands for new services and products in the domain require connectivity, and hence, raise security concerns. Regulators and standardisation bodies started recently to require a structured for security assurance of products in the automotive domain, and automotive companies started, hence, to study ways to create and maintain these cases, as well as adopting them in their current way of working. In order to facilitate the adoption of SAC in the automotive domain, we created CASCADE, an approach for creating SAC which have integrated quality assurance and are compliant with the requirements of ISO/SAE-21434, the upcoming cybersecurity standard for automotive systems. CASCADE was created by conducting design science research study in two iterative cycles. The design decisions of CASCADE are based on insights from a qualitative research study which includes a workshop, a survey, and one-to-one interviews, done in collaboration with our industrial partners about the needs and drivers of work in SAC in industry, and a systematic literature review in which we identified gaps between the industrial needs and the state of the art. The evaluation of CASCADE was done with help of security experts from a large automotive OEM. It showed that CASCADE is suitable for integration in industrial product development processes. Additionally, our results show that the elements of CASCADE align well with respect to the way of working at the company, and has the potential to scale to cover the requirements and needs of the company with its large organization and complex products Copyright © 2024, The Authors. All rights reserved.

关键词： Automotive industry

Rethinking Cognition: Morphological Info-Computation and the Embodied Paradigm in Life and Artificial Intelligence

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arXiv 2024年

作者： Dodig-Crnkovic, Gordana Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Division of Computer Science and Software Engineering School of Innovation Design and Engineering Mälardalen University Västerås Sweden

This study aims to place Lorenzo Magnani’s Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single cells to complex multicellular organisms and their networks. Yet, the literature and general understanding of cognition still largely remain human-brain-focused, leading to conceptual gaps and incoherency. This paper presents a variety of computational (information processing) approaches, including an info-computational approach to cognition, where natural structures represent information and dynamical processes on natural structures are regarded as computation, relative to an observing cognizing agent. We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organization, and autopoiesis across physical, chemical, and biological domains. We examine recent findings linking morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, and active inference. We establish a connection to Magnani’s Eco-Cognitive Computationalism and the idea of computational domestication of ignorant entities. Novel theoretical and applied insights question the boundaries of conventional computational models of cognition. The traditional models prioritize symbolic processing and often neglect the inherent constraints and potentialities in the physical embodiment of agents on different levels of organization. Gaining a better info-computational grasp of cognitive embodiment is crucial for the advancement of fields such as biology, evolutionary studies, artificial intelligence, robotics, medicine, and more. © 2024, CC BY-NC-ND.

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Exploring Cognition through Morphological Info-Computational Framework

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arXiv 2024年

Traditionally, cognition has been considered a uniquely human capability involving perception, memory, learning, reasoning, and problem-solving. However, recent research shows that cognition is a fundamental ability shared by all living beings, from single cells to complex organisms. This chapter takes an info-computational approach (ICON), viewing natural structures as information and the processes of change in these structures as computations. It is a relational framework dependent on the perspective of a cognizing observer/cognizer. Informational structures are properties of the material substrate, and when focusing on the behavior of the substrate, we discuss morphological computing (MC). ICON and MC are complementary perspectives for a cognizer. Information and computation are inseparably connected with cognition. This chapter explores research connecting nature as a computational structure for a cognizer, with morphological computation, morphogenesis, agency, extended cognition, and extended evolutionary synthesis, using examples of the free energy principle and active inference. It introduces theoretical and practical approaches challenging traditional computational models of cognition limited to abstract symbol processing, highlighting the computational capacities inherent in the material substrate (embodiment). Understanding the embodiment of cognition through its morphological computational basis is crucial for biology, evolution, intelligence theory, AI, robotics, and other fields. © 2024, CC BY-NC-ND.

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Predicting compressive strength of sustainable concrete using advanced AI models: DLNN, RF, and MARS

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Asian Journal of Civil engineering 2025年第5期26卷 1939-1954页

作者： Wagh, Monali Waghmare, Charuta Gudadhe, Amit Thakur, Nisha Mohammed, Salah J. Algburi, Sameer Majdi, Hasan Sh. Ansari, Khalid Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Computer Science and Design Division Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India Department of Civil Engineering Bajaj Institute of Technology Wardha India Civil Engineering Department Dijlah University College Baghdad Iraq Al-Kitab University Kirkuk 36015 Iraq Department of Chemical Engineering and Petroleum Industries Al-Mustaqbal University College Babylon 51001 Iraq

Recycled aggregate is becoming a sustainable construction resource that minimizes the carbon footprint in concrete structures. To predict the behavior of environmentally friendly (EnF) concrete in sustainable construction, it is necessary to predict the compressive strength using recycled materials accurately. The current research highlights the performance of the Deep Learning Neural Network (DLNN), Random Forests (RFs), and Multivariate Adaptive Regression Splines (MARS) are evaluated and extensive analysis of data segmentation was performed by splitting the dataset used in this study into 75–25% as well as 80–20% training/testing scenarios using Extreme Gradient Boosting (XG Boost), a quantitative measurement of the effect of data segmentation on model efficiency. The combination of AI models with Extreme Gradient Boosting (XG Boost) was employed to ascertain the governing variables on the CS prediction. Numerous statistical models developed were used to compare the effectiveness of these given models showing the best performance of the DLNN model based on the least RMSE (2.93). The results found that more variables should be added to the prediction problem for better prediction accuracy and the data split of 80–20% was the best choice. Based on the high accuracy of models, the results demonstrated that over the other established models, the DLNN model surpasses them in the analysis of concrete behavior and is useful for future applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Compressive strength prediction Deep learning neural network Multivariate adaptive regression splines Random forests Recycled aggregate XG Boost

FloraWear: Wearable Living Interface 23

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FloraWear: Wearable Living Interface

17th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2023

作者： Nam, Hye Yeon Campbell, Janiece Webb, Andrew M. Harmon, Brendan College of Art and Design Louisiana State University Baton RougeLA United States Epic Games CaryNC United States Division of Computer Science and Engineering Louisiana State University Baton RougeLA United States

ISBN: (纸本)9781450399777

While nature can benefit people both mentally and physically, contemporary society has become increasingly disconnected from nature. To rebuild a stronger connection with nature in our everyday life, we introduce FloraWear, a do-it-yourself, wearable living interface, that enables people to easily and closely connect with plants. This pictorial introduces how knowledge is built and shared with others using hybrid craft and fabrication, illustrates the material experiments and design development for FloraWear, and discusses how it affects wearers. Then we summarize how FloraWear can help catalyze a shift in people's perspectives towards nature. By developing emotional ties to their wearable plants, FloraWear wearers begin to understand that both they and their plants are part of an ecosystem. © 2023 ACM.

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MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR

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arXiv 2024年

作者： Liu, Changkun Zhao, Yukun Braud, Tristan Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Division of Integrative Systems and Design The Hong Kong University of Science and Technology Hong Kong

Recent years have seen significant improvement in absolute camera pose estimation, paving the way for pervasive markerless Augmented Reality (AR). However, accurate absolute pose estimation techniques are computation- and storage-heavy, requiring computation offloading. As such, AR systems rely on visual-inertial odometry (VIO) to track the device’s relative pose between requests to the server. However, VIO suffers from drift, requiring frequent absolute repositioning. This paper introduces MobileARLoc, a new framework for on-device large-scale markerless mobile AR that combines an absolute pose regressor (APR) with a local VIO tracking system. Absolute pose regressors (APRs) provide fast on-device pose estimation at the cost of reduced accuracy. To address APR accuracy and reduce VIO drift, MobileARLoc creates a feedback loop where VIO pose estimations refine the APR predictions. The VIO system identifies reliable predictions of APR, which are then used to compensate for the VIO drift. We comprehensively evaluate MobileARLoc through dataset simulations. MobileARLoc halves the error compared to the underlying APR and achieves fast (80 ms) on-device inference speed. Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality