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42nd Chinese Control Conference, CCC 2023

作者： He, Guangxu Zhao, Jun Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Key Laboratory of Data Analytics Ministry of Education China College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9789887581543

The paper studies the leader-following consensus of the switched uncertain time-delay MASs under fixed directed communication topology. Through the switching distributed controller and the minimum dwell time, the sufficient conditions in terms of matrix inequalities are obtained by L-K functional dependent on switching law to guarantee the leader-following consensus performance. Finally, an example is given to validate the effectiveness of the results. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Time delay

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DiFuseR: A Distributed Sketch-based Influence Maximization Algorithm for GPUs

arXiv

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arXiv 2024年

作者： Göktürk, Gökhan Kaya, Kamer Faculty of Engineering and Natural Sciences Sabancı University Istanbul34956 Turkey Center of Excellence in Data Analytics Sabancı University Turkey

Influence Maximization (IM) aims to find a given number of "seed" vertices that can effectively maximize the expected spread under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are often used for IM. However, these algorithms require a large number of simulations to find good seed sets. In this work, we propose DiFuseR, a blazing-fast, high-quality IM algorithm that can run on multiple GPUs in a distributed setting. DiFuseR is designed to increase GPU utilization, reduce inter-node communication, and minimize overlapping data/computation among the nodes. Based on the experiments with various graphs, containing some of the largest networks available, and diffusion settings, the proposed approach is found to be 3.2× and 12× faster on average on a single GPU and 8 GPUs, respectively. It can achieve up to 8× and 233.7× speedup on the same hardware settings. Furthermore, thanks to its smart load-balancing mechanism, on 8 GPUs, it is on average 5.6× faster compared to its single-GPU performance. © 2024, CC BY.

关键词： Expectation maximization algorithm

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Exact Characterization of the Global Optima of Least Squares Realization of Autonomous LTI Models as a Multiparameter Eigenvalue Problem

Exact Characterization of the Global Optima of Least Squares...

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European Control Conference (ECC)

作者： Sibren Lagauw Lukas Vanpoucke Bart De Moor Department of Electrical Engineering (ESAT) KU Leuven Center for Dynamical Systems Signal Processing and Data Analytics (STADIUS) Leuven Belgium

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We consider the problem of finding the best least squares realization of an autonomous single-output linear time- invariant dynamical system, given a sequence of non-model- compliant output data. We characterize the solution set of the identification problem and derive novel properties of the optimal models. We show how the global minima of the problem follow from the eigentuples of a multiparameter eigenvalue problem and illustrate this result using several numerical 'toy examples' in which we compute the globally optimal solution(s) explicitly.

关键词： Linear systems Computational modeling Toy manufacturing industry Europe Eigenvalues and eigenfunctions Numerical models Dynamical systems

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Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms

Deploying DAPHNE Computational Intelligence on EuroHPC Vega ...

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International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)

作者： Aleš Zamuda Mark Dokter Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia KNOW-CENTER GmbH Research Center for Data-Driven Business & Big Data Analytics Graz Austria

ISBN: (数字)9798350388459

ISBN: (纸本)9798350388466

This paper presents a deployment of DAPHNE (Integrated data Analysis Pipelines for Large-Scale data Management, HPC and Machine Learning) for Computational Intelligence (CI). As CI progresses, e.g. through effort in human societies like the IEEE Computational Intelligence Society (CIS), the latest advances have become more widely popularized. Likewise, with the accessibility of supercomputing resources to advance CI, like with infrastructures such as EuroHPC machines including Vega, this paper demonstrates deployment of Randomised Optimisation Algorithms (ROAs) as a subset of CI algorithms, on EuroHPC Vega. As the Vega is a cluster of distributed nodes, a successful computation on several nodes at once in parallel and concurrently, requires an orchestrated deployment across the nodes, batched using Slurm in this paper. An example ROA benchmarking scenario is then presented in this scope.

关键词： data analysis Pipelines Clustering algorithms Text summarization Machine learning Benchmark testing Timing

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Variational Representation for Risk Assessment in Aggregated Supply Curve Forecasting: A Case Study in Singapore's Electricity Market

Variational Representation for Risk Assessment in Aggregated...

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Sustainable Power and Energy Conference (iSPEC)

作者： Noelle Y. L. Wong Dipti Srinivasan Eng Yeow Cheu Zhonglin Chiam Data Science & Analytics Sembcorp Utilities Pte Ltd Singapore Department of Electrical & Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350395075

ISBN: (纸本)9798350395082

This study underscores the potential of variational representation as a tool for uncertainty estimation in the energy market, which would help Independent System Operators (ISO) in risk assessment and demand response. Forecasts of future supply curves are also very valuable to energy suppliers seeking to optimize bidding strategies in an oligopolistic power market characterized by information privacy and volatility of individual bidding behaviors and variability in renewable energy generation. However, traditional deterministic modelling methods may not adequately capture the inherent uncertainty and volatility of the electricity market. Our variational encoding technique produces a resultant embedding space that is stochastic and exhibits substantial interpretability, which we exploit to demonstrate the model's transparency. By using the learned posterior probability distribution of the embedded aggregated supply curves (ASCs) to determine the statistical dispersion, we identify and study periods of high uncertainty in the Singapore electricity market between December 2021 and March 2023. We seek to provide insights into the origins of these uncertainties, showing that the method can capture changes in the market useful for risk modelling in decision-making.

关键词： Renewable energy sources Uncertainty ISO Decision making Stochastic processes Predictive models Electricity supply industry Probabilistic logic Encoding Risk management

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Application of an Improved Convolutional Neural Network-Based Method in Network Intrusion Detection 3

Application of an Improved Convolutional Neural Network-Base...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Chen, Huan You, Guirong Shiue, Yeouren College of Information Engineering Fujian Business University Fuzhou China Fujian Prov. Universities Eng. Research Center of Big Data Analytics for Business Intelligence Fujian Business University Fuzhou China National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu Taiwan

ISBN: (纸本)9798350308327

This study investigates the use of convolutional neural network (CNN) technology to solve the problem of network intrusion detection, specifically studying the impact of convolutional layer count. Experiments revealed that two convolutional layers yield optimal results. An AdaBoost+CNN model was proposed using this CNN with optimization strategies such as ensemble learning. Eight classical learning algorithms were compared with the proposed methods, and the results were evaluated using parameters such as accuracy, precision, and F-measurement. Compared with traditional machine learning, deep learning, and other methods, the method proposed in this study achieved higher classification accuracy and successful results in detecting network attacks. © 2023 IEEE.

关键词： Adaptive boosting

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Cascaded Noise Reduction and Acoustic Echo Cancellation Based on an Extended Noise Reduction

Cascaded Noise Reduction and Acoustic Echo Cancellation Base...

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European Signal Processing Conference (EUSIPCO)

作者： Arnout Roebben Toon Van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In many speech recording applications, the recorded desired speech is corrupted by both noise and acoustic echo, such that combined noise reduction (NR) and acoustic echo cancellation (AEC) is called for. A common cascaded design corresponds to NR filters preceding AEC filters. These NR filters aim at reducing the near-end room noise (and possibly partially the echo) and operate on the microphones only, consequently re-quiring the AEC filters to model both the echo paths and the NR filters. In this paper, however, we propose a design with extended NR (NRext) filters preceding AEC filters under the assumption of the echo paths being additive maps, thus preserving the addition operation. Here, the NR ext filters aim at reducing both the near-end room noise and the far-end room noise component in the echo, and operate on both the microphones and loudspeakers. We show that the succeeding AEC filters remarkably become independent of the NR ext filters, such that the AEC filters are only required to model the echo paths, improving the AEC performance. Further, the degrees of freedom in the NR ext filters scale with the number of loudspeakers, which is not the case for the NR filters, resulting in an improved NR performance.

关键词： Loudspeakers Filters Echo cancellers Additives Noise Noise reduction Acoustics Recording Standards Microphones

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Efficient Deep Attentive Pixels Network in Face Super-Resolution at Scale Factor of 16

Efficient Deep Attentive Pixels Network in Face Super-Resolu...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Hein Htet Aung Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. Facial attributes have been effectively used to guide low-level information of the face to perform robust face image restoration. Iterative techniques appraised the value of facial landmarks to boost the reconstruction capability of the super-resolution network. Nevertheless, the network parameters in FSR are high, while the learning rate is still low. This paper proposes an attention mechanism combined with the Face Alignment Network (FAN). The proposed attention mechanism consists of channel attention and a non-local module. Our proposed model outperforms at the scale of $\times 16$ compared to the other state-of-the-art models.

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Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

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Overview of the shared task on Fake News Detection from Social Media Text 3

Overview of the shared task on Fake News Detection from Soci...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Subramanian, Malliga Chakravarthi, Bharathi Raja Shanmugavadivel, Kogilavani Pandiyan, Santhiya Kumaresan, Prasanna Kumar Palani, Balasubramanian Singh, Muskaan Raja, Sandhiya Vanaja Mithunajha, S. Kongu Engineering College Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland Indian Institute of Information Technology Kottayam India Ulster University United Kingdom

ISBN: (纸本)9789544520854

The rapid proliferation of fake news has emerged as a significant challenge to the credibility of online information, largely due to the swift dissemination of content on social media platforms. This article provides a concise summary of the findings of the shared task on"Fake News Detection in Dravidian Languages1 - DravidianLangTech@RANLP 2023". The aim of this shared task is to categorize social media posts as either fake or original, specifically focusing on content in Malayalam. The shared task garnered participation from 8 teams who presented their systems. These systems encompassed a spectrum of methodologies including machine learning techniques and transformer-based models like MuRIL, XLMRoBERTa, and Indic BERT. Notably, the XLMRoBERTa-based model demonstrated exceptional performance, achieving a macro F1-score of 0.90. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.

关键词： Learning systems

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