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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pan, Haochen Chard, Ryan Zhou, Sicheng Kamatar, Alok Vescovi, Rafael Hayot-Sasson, Valerie Bauer, Andre Gonthier, Maxime Chard, Kyle Foster, Ian Southern University of Science and Technology Department of Computer Science China Illinois Institute of Technology Department of Computer Science ChicagoIL United States University of Chicago Department of Computer Science ChicagoIL United States Argonne National Laboratory Data Science and Learning Division LemontIL United States

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients. © 2024 IEEE.

关键词： Event-driven architecture research automation scientific computing

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Switching Control for Identification Deception in Cyber-Physical Systems 63

Switching Control for Identification Deception in Cyber-Phys...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Mavridis, Christos Kanellopoulos, Aris Sandberg, Henrik Johansson, Karl Henrik Kth Royal Institute of Technology Division of Decision and Control School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching. © 2024 IEEE.

关键词： Markov processes

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Machine Learning Diagnosis of Lumpy Skin Disease in Cattle Herds 3

Machine Learning Diagnosis of Lumpy Skin Disease in Cattle H...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishna, Nimmagadda Kumar Benjamin, Ruth Moly Tejaswi, Nimmagadda Institute of Technology and Sciences Division of Computer Science and Engineering Karunya Coimbatore India Shri Vishnu Engineering College for Women Division of Artificial Intelligence and Data Science Bhimavaram India

ISBN: (纸本)9798350375190

In order to diagnose lumpy skin disease in cattle herds, machine learning techniques such as Support Vector Machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective of the study is to create a diagnostic tool that would be both accurate and effective to identify and treat this infectious disease early on. The algorithms were trained to classify cases with high accuracy using a dataset that included different clinical and demographic characteristics related with the presence or absence of Lumpy Skin Disease. SVM is used because of its capacity to manage intricate and non-linear correlations in the data, and Random Forest and Gradient Boosting algorithms were chosen due to their predictive performance. © 2024 IEEE.

关键词： Support vector machines

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Advancing Hate Speech Detection: A Multilingual System Using LLAMA-2 for Real-Time Analysis of Audio, Video, and Text Content

Advancing Hate Speech Detection: A Multilingual System Using...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Talha, M.D. Rexie, J.A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331521394

This paper introduces an advanced, multilingual hate speech detection system designed to process various media formats, including audio, video, and text. Unlike traditional detection models that focus solely on text analysis, our system leverages OpenAI's Whisper model to convert audio and video content into text, enabling a broader scope of analysis. At its core, the system utilizes a fine-tuned Llama-2 model specifically trained for hate speech classification. The detection system is accessible through a user-friendly Streamlit web application, providing real-time processing and feedback. Results demonstrate that this comprehensive, multimedia approach significantly enhances detection accuracy and usability, marking a notable advancement in the field. © 2025 IEEE.

关键词： Speech enhancement

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DETR-SAM: Combining Feature-Fused DETR Detector and Fine-Tuned SAM for Automatic Aortic Dissection Segmentation in CTA Image

DETR-SAM: Combining Feature-Fused DETR Detector and Fine-Tun...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cai, Wenjie Tang, Tao Kong, Lingming Zhou, Ying Huang, Jun Wang, Qingfeng Li, Jing University of Science and Technology of China School of Computer Science and Technology Hefei China Southwest University of Science and Technology School of Computer Science and Technology Mianyang China University of Science and Technology of China The First Affiliated Hospital of Ustc Division of Life Sciences and Medicine Department of General Surgery Hefei China Mianyang Central Hospital Department of Radiology Mianyang China

ISBN: (纸本)9798350386226

Aortic Dissection (AD) is a life-threatening disease. As such, it's vital to make clinical strategies by accurate segmentation of AD from Computed Tomography Angiography (CTA) images. Due to the heterogeneous morphology of AD in CTA images and the influence of image noise, inaccurate segmentation results are often produced, making it challenging to accurately identify the contours of AD. To meet these challenges, we propose a novel automatic AD segmentation model DETR-SAM based on the Segment Anything Model(SAM). Our model consists of a deformable DETR object detector module with multi-scale feature fusion and a fine-tuned SAM module for refining the segmentation contours. The object detector module effectively fuses the multi-scale image features extracted by the image encoder of SAM with the features obtained from the DETR backbone CNN network. The fused multi-scale features generate multiple prompts rich in geometric shape knowledge through multiple regression prediction heads to guide the SAM model to refine the segmentation boundaries and enhance the recognition of contours. Meanwhile, a fine-tuning strategy using these prompts is proposed to enhance the SAM model's understanding of AD features. This strategy enables our DETR-SAM model with a 2D segmentation network architecture to surpass the performance of mainstream 3D segmentation algorithms. We have evaluated DETR-SAM on the ImageTBAD dataset and our internal dataset. The results showed that the accuracy of our model is better than the current state-of-the-art 2D and 3D segmentation methods in all four indicators. In addition, the effectiveness of the feature fusion module, hint generation method and fine-tuning strategy are further verified by ablation experiments. Our DETR-SAM method is helpful to a certain extent in the diagnosis and auxiliary treatment of AD in different clinical situations and lays a foundation for further research and clinical application. © 2024 IEEE.

关键词： Image segmentation

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Deciphering DNA variant-associated aberrant splicing with the aid of RNA sequencing (vol 55, pg 732, 2023)

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NATURE GENETICS 2023年第7期55卷 1251-1251页

作者： Zhang, Bin Gao, Xin Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia KAUST Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia

Aberrant RNA splicing events resulting from DNA variations are common causes of genetic disorders. Two studies published in Nature Genetics independently describe methods to decipher DNA-variant-associated aberrant sp... 详细信息

关键词： Data mining High-throughput screening

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Enhancement of Photovoltaic Dust Detection Using Deep Learning Technique

Enhancement of Photovoltaic Dust Detection Using Deep Learni...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Gracia, R. Lillian Priyadharsini, C. Caroline, B.Elizabeth Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Ifet College Of Engineering Division Of Electronics And Communication Engineering Villupuram India

ISBN: (纸本)9798350371567

The dust collection on solar panels situated in solar parks is a common problem faced by PV system operators, particularly in arid and dusty regions. Dust deposition reduces the transparency of the panel surface, leading to decreased sunlight absorption and thus reduced energy conversion efficiency. Traditional methods for detecting dust accumulation involve manual inspection or periodic cleaning schedules, both of which are labor-intensive, time-consuming, and often inefficient. Recent developments in deep learning and machine vision have made autonomous dust detection techniquesfeasible. Numerous techniques have been employed to identify and measure dust on solar panels, such as machine learning models and image processing algorithms. In this paper, the dataset of clean and dusty images of the solar panels utilized wasapplied to four state of the art models and then the EfficientnetB0 model was proposed to detect the dust over the solar panels. On comparing the EfficientnetB0 model with the other state of the art models, EfficientnetB0 yielded an impressive accuracy rate of approximately 85.92%. Further research and development in this field hold the potential to revolutionize the maintenance and operation of solar power plants, contributing to a greener and more sustainable future. © 2024 IEEE.

关键词： Solar panels

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Mitigating Light Detection and Ranging Spoofing Attacks in Connected Vehicle Systems Using Machine Learning-Based Anomaly Detection 2

Mitigating Light Detection and Ranging Spoofing Attacks in C...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Karthik, R. Marshal, S. Asmitha Shree, R. Vidhya, R. Saranya, N. Gomathy, A. Karunya Institute of Technology and Sciences Deemed to be University Division of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Light Detection and Ranging (LiDAR) technology is one of the integral parts of systems involving connected vehicles. It provides the necessary two-dimensional accuracy to enable proper navigation, object identification, and collision avoidance through LIDAR technology. However, it is highly used and highly susceptibility to spoofing attacks: interference on signals radiated by LIDAR technology to confuse the Vehicle Perception System. This paper proposes a machine learning-based anomaly detection framework which is capable of discernment and countering of spoofing attacks. The proposed framework collects data from a set of sensors, including cameras, radar, and LiDAR, and is used to formulate a holistic, multidimensional perception model. In that sense, the system utilizes advance machine learning methods, like ensemble classifiers, neural networks, and, more importantly, autoencoders, to find anomalies in real-time that might point to spoofing activity. Simulations and empirical experiments, both benign and adversarial, evaluate the systematic approach to spoofing sophistication levels where LiDAR systems indicate noteworthy improvements in detection accuracy and robustness compared with the traditional techniques, thereby reducing false positives and negatives. In addition, through sensor fusion, the system demonstrates resilience by sustaining consistent performance even through the most complex and dynamic environments. This type of study really highlights that how the power of machine learning would be important for improvement in the cybersecurity framework of connected vehicles scalable and adaptable toward the protection of LiDAR-based systems from growing threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Solar PV Deployment: Land Suitability Assessment and Site Selection using AHP

Enhancing Solar PV Deployment: Land Suitability Assessment a...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Tryphena, Blessy Priyadharsini, C. Vidhya, J. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Ifet College Of Engineering Division Of Electronics And Communication Engineering Villupuram India

ISBN: (纸本)9798350371567

In response to the escalating global energy demand driven by population growth and technological advancements, solar PV emerges as a competitive and viable renewable energy source. [9] With the International Renewable Energy Agency (IRENA) projecting a significant increase in solar PV capacity by 2030 and a dramatic reduction in installation costs, the importance of strategic site selection for solar installations cannot be overstated. This paper emphasizes the necessity of conducting a thorough land suitability analysis to optimize energy output and minimize costs, taking into account environmental, economic, and social factors. To achieve this, the study employs Analytic hierarchy process within an Multicriteria decision making framework, coupled with GIS analysis, to systematically evaluate potential sites for large-scale solar power projects. This paper contributes to the field by providing a comprehensive approach to selecting optimal sites for solar PV installations, combining spatial analysis with evaluative and ranking capabilities. Through this integrated approach, the study underscores the potential of solar PV technology as a cornerstone of future energy systems, highlighting the critical role of precise site selection in realizing the full benefits of solar energy deployment. © 2024 IEEE.

关键词： Geographic information systems

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Nemeth Braille Mathematical Code Recognition using Deep Learning Techniques

Nemeth Braille Mathematical Code Recognition using Deep Lear...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Malar, P. Jeba Raimond, Kumudha Kanmani, S. Deepa Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Sri Krishna College Of Engineering And Technology Department Of Information Technology Coimbatore India

ISBN: (纸本)9798350371567

The Nemeth Braille Mathematical Code (NBMC) is an essential resource for individuals with visual impairments to comprehend scientific and mathematical symbols. It enables them to understand intricate mathematical equations, symbols, and declarations. Still, there are many challenges in accurately identifying NBMC. This makes it more difficult for those with vision problems to learn information and be self-driven. In this work, a deep learning (DL) based approach is proposed for recognizing NBMC. Comparisons are made between different deep learning models such as convolutional neural network (CNN), InceptionV3, DenseNet201, CNN combined with ResNet50, ResNet50, and VGG16;where CNN combined with ResNet50 model provides the best accuracy among them with a value of 91.20%. Then by using the layers of CNN model combined with ResNet50 works as backend for the web application. The NBMC are then translated into text-to-speech by the web application, to make it easily accessible to visually challenged people. © 2024 IEEE.

关键词： Convolutional neural networks

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