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Machine Learning-driven Antenna Selection Strategies for Fading MIMO systems

Machine Learning-driven Antenna Selection Strategies for Fad...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), international conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice.

关键词： measurement Hands Adaptation models Machine learning algorithms Computational modeling Neural networks Reinforcement learning Throughput Real-time systems Signal to noise ratio

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Exploiting Kubernetes Autoscaling for Economic Denial of Sustainability 25

Exploiting Kubernetes Autoscaling for Economic Denial of Sus...

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Abstracts of the 2025 ACM SIGMETRICS international conference on measurement and modeling of computer systems

作者： Jonathan Chamberlain Jilin Zheng Zeying Zhu Zaoxing Liu David Starobinski Department of Electrical and Computer Engineering Boston University Boston Massachusetts USA Department of Electrical and Computer Engineering Boston University Boston University USA Department of Computer Science University of Maryland College Park Maryland USA

ISBN: (纸本)9798400715938

The complexity required to support the flexibility and scale of networks achievable by Kubernetes (K8s)-based applications leaves them vulnerable to Economic Denial of Sustainability (EDoS) attacks attempting to deprive targets of the financial means to sustain themselves. We develop Markov Decision Process (MDP) based models to reason about autoscaling and the components influencing scaling thresholds, and build atop the MDP a Stackelberg game structure to reason about EDoS attacks on K8s autoscaling and the circumstances under which adversaries inject traffic. Through numerical evaluations, we show examples where hourly-based charges eliminate incentives for resource scale-down, increasing attack incentive. Through the use of experiments on a realistic K8s cluster, we further show that increasing the attack intensity may render it less effective in generating surplus K8s resources compared to the increased traffic generation cost.

关键词： economic denial of sustainability

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Integrating I/O Time to Virtual Time System for High Fidelity Container-based Network Emulation 22

Integrating I/O Time to Virtual Time System for High Fidelit...

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2022 ACM SIGSIM international conference on Principles of Advanced Discrete Simulation, SIGSIM PADS 2022

作者： Chen, Gong Hu, Zheng Jin, Dong Computer Science Illinois Institute of Technology ChicagoIL United States Computer Science and Computer Engineering University of Arkansas FayettevilleAR United States

ISBN: (纸本)9781450392617

Container-based network emulation provides an accurate, flexible, and cost-effective application design and evaluation testing environment. Enabling virtual time to processes running inside containers essentially improves the temporal fidelity of emulation experiments. However, the lack of precise time management during operations, such as disk I/O, network I/O, and GPU computation, often leads to virtual time advancement errors observed in existing virtual time systems. This paper proposes VT-IO, a lightweight virtual time system that integrates precise I/O time for container-based network emulation. We model and analyze the temporal error during I/O operations and develop a barrier-based time compensation mechanism in the Linux kernel. VT-IO enables accurate virtual time advancement with precise I/O time measurement and compensation. The experimental results show that the temporal error can be reduced from 87.31% to 3.6% and VT-IO only introduces around 2% overhead of the total execution time. Finally, we demonstrate VT-IO's usability and temporal fidelity improvement with a case study of a Bitcoin mining application. © 2022 ACM.

关键词： Linux Container Virtual Time Virtualization Network Emulation TimeKeeper

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Data-Driven Discovery of Nonlinear Dynamical systems from Noisy and Sparse Observations

Data-Driven Discovery of Nonlinear Dynamical Systems from No...

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New Trends in Computational Intelligence (NTCI), international conference on

作者： Wei Zhu Chao Pei Yulan Liang Zhang Chen Jingsui Li School of Science Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven algorithm for interpretability mod-eling. SINDy has a strong ability to learn dynamical systems from time series data. Sparse Identification of Nonlinear Dynamics (SINDy) can extract equations from data, but its performance will degrade when data has noise or sparse. In order to deal with this case, the idea of Monte Carlo Markov Chain (MCMC) is introduced, which is used to optimally select models. The SINDy with MCMC (MCMC-SINDy) can comprehensively discover model structures by employing random samples in data space. Simulation results show that MCMC-SINDy is more robust than SINDy with 1 % -15 % levels of noise or sparse 0.5. These results are promising for the discovery of governing equations for complex systems in neuroscience, power grids, epidemiology, finance or ecology, where governing equations have remained elusive. mportantly, compared to existing machine learning methods, the accuracy of modeling using MCMC SINDy is higher.

关键词： Neuroscience Biological system modeling Simulation Noise Time series analysis Mathematical models Power grids Nonlinear dynamical systems Noise measurement Periodic structures

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Comparative Analysis of Machine Learning-Based Surrogate modeling Approaches for Multi-Body Dynamic Simulation in Railway Digital Twin Platform

Comparative Analysis of Machine Learning-Based Surrogate Mod...

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IEEE international conference on Industrial Engineering and Engineering Management

作者： Shiyang Zhou Artur Sogomonyan Artur Ohanian William Amminger Yuxi Xia Manfred Grafinger Institute of Engineering Design and Product Development Vienna University of Technology Lehargasse 6/307 Vienna Austria Faculty of Computer Science University of Vienna Vienna Austria

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

Machine learning (ML)-based surrogate models offer a promising alternative for Multibody Dynamics (MBD) Simulation of railway vehicle-track dynamics systems. A well-built ML model can accurately and quickly predict the dynamic responses to various track irregularities, significantly reducing computation time and unifying integration interfaces of different vehicle-track systems. However, training effective surrogate models is a complex process, influenced by the specific needs of the analysis. Different algorithms and training sets might be required for different surrogate models, making it essential to research the impact factors for building these models. This paper presents a comparative analysis of ML-based surrogate modeling approaches tailored for MBD Model within a railway digital twin platform. The primary focus is evaluating the performance of various ML-based surrogate modeling approaches, as well as the influence of neural networks, training parameters, and data sources. By leveraging extensive simulation and measurement data, we assess the ability of these surrogate models to predict key performance indicators under varying operational conditions. This analysis provides valuable insights for railway engineers in selecting appropriate surrogate modeling approaches, ultimately contributing to the advancement of predictive maintenance and optimization in railway operations.

关键词： Training Analytical models Correlation Computational modeling Predictive models Rail transportation Data models Digital twins Vehicle dynamics Optimization

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A Small-Footprint Keyword Spotting modeling with Novel Data Augmentation Methods

A Small-Footprint Keyword Spotting Modeling with Novel Data ...

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international joint conference on Neural Networks (IJCNN)

作者： Wei-Kai Huang Kuan-Yu Chen Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Keyword spotting (KWS) system is an important human-computer interaction medium in smart devices. However, requiring the KWS model to maintain robust performance with a small number of parameters is extremely challenging. In addition to model architecture research, efficiently leveraging speech data to develop more powerful KWS systems is also an attractive research topic. In the speech-related community, various data augmentation methods have been studied. Mainstream methods mainly focus on manipulating speech waveforms or acoustic features. Regardless of the approach taken, a simple conclusion is that data augmentation strategies can further improve task performance. Motivated by practical needs and following interesting research lines, on the one hand, the paper presents an efficient KWS model that is designed to leverage the spatial and channel information of the input speech simultaneously while keeping the lightweight property of the model. On the other hand, we develop two novel data augmentation methods for training the KWS model to boost performance further. A series of experiments are conducted using the Google Speech Commands V2 dataset to evaluate the proposed KWS framework. Based on the experiments, the proposed framework can achieve 98.81% accuracy with fewer parameters than other state-of-the-art models.

关键词： Training Performance evaluation Human computer interaction Neural networks Speech recognition Benchmark testing Data augmentation

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Optimizing Monocular Depth Estimation through Bi-Level Nested Architecture Integration

Optimizing Monocular Depth Estimation through Bi-Level Neste...

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international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Faiaz Hasanuzzaman Rhythm Tareque Bashar Ovi Nomaiya Bashree Md. Raisul Islam Ratul Hussain Nyeem Md Abdul Wahed Deptartment of EECE Military Inst. of Sci. and Tech Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Monocular depth estimation (MDE) has evolved into a vital research focus in visual computing and autonomous robotic systems. While recent deep learning approaches have advanced the field, existing methods often struggle to jointly incorporate global context and fine-grained local details, and may be hampered by complex, computationally expensive network architectures. To address these challenges, we employ the U 2 Net framework, which incorporates Residual U-Blocks designed to adapt the area of reception and capture multi-scale contextual data. By nesting U-shaped structures, U 2 Net effectively increases feature extraction depth while maintaining computational efficiency, thus offering a more balanced and scalable solution for MDE. Our proposed model, developed using the NYUDepth V2 dataset, exceeds contemporary methods in critical assessment measures, including RMSE, RMSE(log), and Absolute Relative Error (Abs Rel). These improved results highlight the robustness and precision of proposed model or a range of practical applications in robotics, augmented reality, and other domains requiring real-time scene geometry understanding.

关键词： Deep learning Adaptation models Visualization Depth measurement Computational modeling Reliability engineering Robustness Real-time systems Computational efficiency Robots

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A Topic modeling for ALICE'S Log Messages using Latent Dirichlet Allocation 5

A Topic Modeling for ALICE'S Log Messages using Latent Diric...

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5th IEEE international conference on Knowledge Innovation and Invention, ICKII 2022

作者： Prayurahong, Pattapon Phunchongharn, Phond Barroso, Vasco Chibante King Mongkut's University of Technology Thonburi Computer Engineering Department Bangkok Thailand European Organization for Nuclear Research Geneva Switzerland

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In modern-day software where digital technology is everywhere, the system can generate a massive amount of log messages every second. Like other data, a log can provide insight and depth knowledge of the system given enough resources and time. However, not all systems have an organized log system, and an unorganized log is messy and difficult to navigate. There are many challenging points for organizing the log messages. As the amount of log data generated is massive, it is impossible to be handled by human labor alone. A log message is not regular human communication. To thoroughly understand the content inside the log, assistance from specialists of that particular system is required. These problems exist everywhere, and there is no exception even for high-performance computing systems like those used in the ALICE experiment at CERN. In this paper, we propose a topic modeling for ALICE's log messages using the Latent Dirichlet Allocation algorithm. The objective is to convert the messy log messages into categorized ones. We handled the log messages and preprocessed them using Bag of Word. Then we performed hyperparameter-tuning to find the suitable number of topics using topic coherence as an evaluated measurement. Additionally, we also applied the same method to the log dataset of HDFS, to ensure the valid ability of the model. Finally, the outputs were then handed to CERN domain experts to give the final evaluation. From the result, we could create a practical topic modeling framework for ALICE's log messages in a real scenario. © 2022 IEEE.

关键词： Statistics

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computer Vision Based measurement of Yellow Water Transparency in Fermentation of Chinese Baijiu Brewing Process

Computer Vision Based Measurement of Yellow Water Transparen...

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IEEE international Workshop on Imaging systems and Techniques (IST)

作者： Xing Chen Li Cao Pan Song Suyi Zhang Defu Xu Lihui Peng Department of Automation Tsinghua University Beijing China LuZhou LaoJiao Co.Ltd. Tsinghua University-LuZhou LaoJiao Joint Research Center for Intelligent Detection Luzhou China Department of Automation Tsinghua University-LuZhou LaoJiao Joint Research Center for Intelligent Detection Tsinghua University Beijing China

Yellow water is a by-product of Chinese Baijiu brewing process. Monitoring various physical and chemical parameters of yellow water can distinguish the fermentation process and grasp the quality of the process, of which, transparency is one of the important indicators for the evaluation of Chinese Baijiu fermentation. Traditional methods for measuring the transparency of yellow water rely too much on manual experience, which are too subjective and have large detection errors. In this paper, a system for measuring the transparency of yellow water by using computer vision based imaging processing is designed to implement the classical method of Secchi disc transparency measurement. The system takes images of Secchi disc under the occlusion of yellow water at different liquid levels and calculates the occlusion degree in each image. Eventually, a transparency function model corresponding to each liquid is established, realizing the quantification of yellow water transparency. At the same time, the corresponding testing device has the advantages of small size, low cost, easy portability, simple operation and rapid measurement speed. The experimental results show that the transparency of yellow water can be measured accurately and that the transparency measured from our proposed system has a high degree of agreement with the human eye observation.

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Visual Analysis of the Bibliometric Data Associated with the Calibration of Car-Following Models

Visual Analysis of the Bibliometric Data Associated with the...

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international conference on Distributed Computing in Sensor systems (DCOSS)

作者： Mădălin-Dorin Pop Mihai V. Micea Computer and Information Technology Department Politehnica University of Timişoara Timişoara România

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Intelligent transportation systems (ITS) use the latest technologies for real-time traffic control and monitoring to ensure efficient traffic management and reduce the risks of traffic accidents. The microscopic level of traffic modeling is the most appropriate level for controlling and monitoring the interaction between vehicles based on car-following scenarios. However, the data retrieved from sensor networks can be affected by measurement errors, and consequently the implementation of appropriate mechanisms to overcome their propagation to the control system is mandatory. This paper aims to analyse the current research in the calibration of car-following models and provide valuable insights of recent developments in this field. To achieve this goal, VOSviewer has been chosen as a visualisation tool to create bibliographic maps based on the output from the well-known scientific database Clarivate Analytics Web of Science (WoS). The maps obtained provide a visual representation of the main institutions involved in this field of research and identify the research interests based on author and indexing keywords. Furthermore, this paper analyses the top five clusters identified based on the analysis of co-occurrence keywords, presenting discussions about the connections existing within these clusters.

关键词： Analytical models Visualization Computational modeling Bibliometrics Transportation Traffic control Data models

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