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Serverless Machine Learning Platform: A Case for real-time Crisis Detection over Social Media 2

Serverless Machine Learning Platform: A Case for Real-Time C...

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2nd international conference on Computer Science, Engineering and applications, ICCSEA 2022

作者： Jambi, Sahar King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Jeddah Saudi Arabia

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Serverless computing is an emerging execution model in the cloud in which all required infrastructure and resources are fully managed by cloud providers. The promising features of serverless technology have created a new research focus on building the next generation serverless machine learning. In this paper, we propose a serverless machine learning platform for serving models in real-time by leveraging Google serverless cloud services. We evaluate the proposed platform by serving an NLP model built for crisis detection over Twitter platform. Our experimental results substantiate that serverless machine learning provides lightweight and cost effective cloud services for building highly scalable applications for real-time ML model predictions. © 2022 ieee.

关键词： Machine learning

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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramanan, S.V. Rajasekar, M. Shanmugapriya, G. Sujitha, R. Akshya, J. Choudhry, Mani Deepak P Pg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Vsb college of Engineering Department of Computer Science and Engineering Technical Campus Coimbatore India Dr.N G.P Institute of Technology Department of Computer Science and Business Systems Coimbatore India Christ The King Engineering College Department of Information Technology Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance. © 2025 ieee.

关键词： Object tracking

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Navigating Exascale Operational Data Analytics: From Inundation to Insight

Navigating Exascale Operational Data Analytics: From Inundat...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Shin, Woong Osborne, Tim Karimi, Ahmad Maroof Palumbo, Rachel May, Alex Lester, Corwin Hines, Jesse Sattar, Naw Safrin Huk, Leah Simmerman, Scott Brewer, Wesley Miller, Jeffrey Adamson, Ryan Kuchar, Olga Prout, Ryan Wang, Feiyi Atchley, Scott Oral, Sarp Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350355543

In this paper, we address the challenges in achieving sustainable data-driven efficiency by providing a detailed exploration of the end-to-end operational data analytics (ODA) framework that evolved through two generations of supercomputer systems at the Oak Ridge Leadership computing Facility (OLCF). This framework addresses large data streams ingested from heavily instrumented HPC environment that accumulates multi-terabytes per day. We outline the multifaceted data life cycle across HPC procurement, operations, and research & development, identifying key obstacles and design decisions that shape effective strategies in building and supporting data pipelines end-to-end. By sharing key insights and lessons learned from our experience, we offer recommendations for the HPC community on enabling sustainable operational data analytics and beyond. Our contributions aim to bridge the gap between potential and real benefits of operational data, guiding future efforts towards integrated and sustainable operational intelligence in high-performance computing environments. © 2024 ieee.

关键词： Data Analytics Data Governance HPC Post-Exascale Challenges Machine Learning applications Monitoring Operational Data Analytics Telemetry Visual Analytics

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*** Framework for Automated Code Scaling and Execution on Multiple Distributed Machines 18

*** Framework for Automated Code Scaling and Execution on Mu...

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18th ieee international conference on Computer Science and Information Technologies, CSIT 2023

作者： Chaplia, Oleh Klym, Halyna Lviv Polytechnic National University Department of Specialized Computer Systems Lviv Ukraine

ISBN: (纸本)9798350360462

Microservices is an architectural approach in software development where the complex solution is created from small, independently deployable services that communicate through defined APIs. Microservices find applications in many domains, including banking, finance, healthcare, e-commerce, government, military, logistics, and gaming. Microservice architecture provides general principles of how the solution containing small services should *** research proposes a *** framework for automated code sharing between services and execution in the environment of a distributed system. The proposed framework built on top of *** contains a tiny code base and mechanisms for sharing the user-developed code among many instances of the microservices inside one *** was selected for this framework because it is a tiny framework and library for building server applications. Also, it can be run on embedded devices, cell phones, or any device with a small OS. © 2023 ieee.

关键词： Cloud computing architecture

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Leveraging Latent Temporal Features for Robust Fault Detection and Isolation in Hexacopter UAVs 10

Leveraging Latent Temporal Features for Robust Fault Detecti...

引用

10th international conference on Automation, Robotics, and applications, ICARA 2024

作者： Sehgal, Shivaan Maniar, Aakash Kandath, Harikumar Gangadharan, Deepak IIIT-Hyderabad Robotics Research Center Hyderabad India IIIT-Hyderabad Computer Systems Group Hyderabad India

ISBN: (纸本)9798350394245

This paper introduces a novel approach for fault detection and localization in a motor of a Hexacopter UAV. The proposed two-stage architecture leverages Long Short-Term Memory (LSTM) [1] networks for latent temporal feature extraction and Random Forest [2] for localization. By combining them, we see improved fault detection and isolation performance. Our evaluations show the robustness of this approach in varying noise levels and real-world-like environments. Analysis of computational efficiency in rapid detection shows that the model identified faults within 2-5 time steps of the flight. Finally, we show that the proposed method surpasses classical statistical models and deep learning techniques in terms of overall accuracy (96.78 %). © 2024 ieee.

关键词： Long short-term memory

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Design and Signal Quality Assessment of a Novel Low-Cost Wireless EMG Sensor 14

Design and Signal Quality Assessment of a Novel Low-Cost Wir...

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14th ieee international conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Ling, Ziqin Chen, Jiangcheng Li, Yaonan Xi, Ning University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong Shenzhen Academy of Robotics Shenzhen China

ISBN: (纸本)9798331506056

Electromyography (EMG) sensors have been widely used for assessing muscle activation state and human-robot interaction (HRI). However, available commercial EMG sensors are expensive and inconvenient for real-time applications. The aim of this study is to design and validate a novel low-cost wireless EMG sensor. Specifically, the wireless EMG sensor is equipped with an analog-front-end (AFE), radio frequency (RF) module, and controller area network (CAN) bus, which achieves wireless data transmission and provides an interface for real-time HRI. The evaluation of the designed system was done by a series of exercises performed by volunteers. Raw signal., mean absolute value (MAV), zero crossings (ZC), slope sign changes (SSC), waveform length (WL), and median frequency (MDF) are used for comparing wireless EMG sensors and commercial sensors. In addition, Spearman's correlation (SC), and intra-class cross-correlation (ICC) are used as two validation indicators. The result shows that the SC and ICC between the raw signals of the two types of sensors are 0.6156 and 0.7780, respectively. The SC of MAV, ZC, SSC, WL, and MDF are 0.9708, 0.6740, 0.8023, 0.9663, and 0.9476, respectively. The ICC of MAV, ZC, SSC, WL, and MDF are 0.9670, 0.7340, 0.7910, 0.9540., and 0.8230, respectively. The results indicate a good agreement between the two systems and confirm the reliability of the wireless EMG sensor's design. The wireless EMG sensor can be further applied to real-time HRI and muscle fatigue assessment. © 2024 ieee.

关键词： Human robot interaction

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applications, Insights and Implications of Digital Twins in Smart City Management 12

Applications, Insights and Implications of Digital Twins in ...

引用

12th international conference on Smart Grid (ICSmartGrid)

作者： Ersan, Mustafa Irmak, Erdal Colak, Alperen Mustafa Gazi Univ Grad Sch Nat & Appl Sci Dept Elect & Elect Engn Ankara Turkiye Gazi Univ Fac Technol Dept Elect & Elect Engn Ankara Turkiye Toshiba Mitsubishi Elect Ind Syst Corp Ind & Energy Syst Div Tokyo Japan

ISBN: (纸本)9798350361612;9798350361629

Digital twins (DTs) represent a transformative paradigm in urban management, leveraging computer modelling and real-time data integration to create virtual replicas of physical entities and systems. This paper explores their multifaceted applications, emphasizing efficiency, sustainability, and resilience. Synthesizing insights from diverse disciplines, it delineates the process of building digital twins and underscores their role in predictive maintenance, optimized performance, and data-driven decision-making. Using a smart city example, the paper illustrates how digital twins facilitate proactive interventions, resource optimization, and informed decision-making in domains such as traffic management, energy optimization, and public safety. It highlights the significance of digital twins in fostering collaboration, innovation, and resilience, empowering cities to address complex challenges and create inclusive, sustainable environments. In conclusion, digital twins emerge as indispensable tools in realizing the vision of smart, connected, and adaptive cities, poised to navigate the complexities of the contemporary urban landscape and pave the way for a sustainable future.

关键词： Digital Twin Smart City Urban Management

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Predicting time Series of the Lorenz Chaotic System Using Random Forest Regression 8

Predicting Time Series of the Lorenz Chaotic System Using Ra...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Li, Yuxuan University of Jinan School of Mathematical Sciences Shandong Jinan China

ISBN: (纸本)9798350302455

This paper presents a novel approach for predicting Lorenz chaotic sequences using machine learning algorithms. The proposed research has broad applications in studying the chaotic properties of nonlinear sequences, predicting financial market trends, and has significant academic and commercial value. Chaotic sequences are highly unpredictable nonlinear sequences, and their behavior is extremely sensitive to initial value changes. Chaotic maps have found applications in various fields, such as physics, encrypted communication, and financial market prediction. The Lorenz chaotic system is a classical chaotic system that has been widely used in science and engineering. In this study, we utilized the random forest regression technique in machine learning to predict the time series produced by the Lorenz system. Our experimental results demonstrate that the machine learning algorithm can accurately predict the chaotic sequences, which enhances our understanding of these nonlinear sequences and enables us to apply them to chaotic systems more effectively. This research provides valuable insights into the dynamics of the Lorenz chaotic system, and the proposed approach has the potential to be extended to other chaotic systems. © 2023 ieee.

关键词： Chaotic systems

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QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing

QUEST: Quality-informed Multi-agent Dispatching System for O...

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ieee conference on Computer Communications (ieee INFOCOM)

作者： Li, Zuxin Man, Fanhang Chen, Xuecheng Xu, Susu Dang, Fan Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China Johns Hopkins Univ Dept Civil & Syst Engn Baltimore MD USA Pengcheng Lablatory Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798350383515;9798350383508

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

关键词： Internet of Things Mobile sensing and applications Mobile Crowdsensing Data Quality

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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 ieee.

关键词： Machine learning

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