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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain

arXiv

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arXiv 2024年

作者： Zyblewski, Pawel Klikowski, Jakub Borek-Marciniec, Weronika Ksieniewicz, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it is assumed that deep learning methods are not the optimal solution for application in this field. However, excluding the entire – and prevalent – group of methods seems rather rash given the progress that has been made in recent years in its development. For this reason, the following paper is the first to present an approach to natural language data stream classification using the sentence space method, which allows for encoding text into the form of a discrete digital signal. This allows the use of convolutional deep networks dedicated to image classification to solve the task of recognizing fake news based on text data. Based on the real-life Fakeddit dataset, the proposed approach was compared with state-of-the-art algorithms for data stream classification based on generalization ability and time complexity. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Analysis of Extractive Text Summarization Methods as a Binary Classification Problem 1

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16th International Conference on Computational Intelligence in Security for Information systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023

作者： Komorniczak, Joanna Wojciechowski, Szymon Klikowski, Jakub Kozik, Rafal Choraś, Michal Pattern Recognition Team of Department of Computer Systems and Networks Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Bydgoszcz University of Science and Technology Al. prof. S. Kaliskiego 7 Bydgoszcz85-796 Poland

ISBN: (数字)9783031425196

ISBN: (纸本)9783031425189

One of the critical challenges for natural language processing methods is the issue of automatic content summarization. The enormous increase in the amount of data delivered to users by news services leads to an overload of information without meaningful content. There is a need to generate an automatic text summary that contains as much essential information as possible while keeping the resulting text smooth and concise. Methods of automatic content summarization fall into two categories: extractive and abstractive. This work converts the task of extractive summarization to a binary classification problem. The research focused on analyzing various techniques for extracting the abstract in a supervised learning manner. The results suggest that this different view of text summarization has excellent potential. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Text processing

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Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification

arXiv

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arXiv 2024年

作者： Zyblewski, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

Rapid technological advances are inherently linked to the increased amount of data, a substantial portion of which can be interpreted as data stream, capable of exhibiting the phenomenon of concept drift and having a high imbalance ratio. Consequently, developing new approaches to classifying difficult data streams is a rapidly growing research area. At the same time, the proliferation of deep learning and transfer learning, as well as the success of convolutional neural networks in computer vision tasks, have contributed to the emergence of a new research trend, namely Multi-Dimensional Encoding (MDE), focusing on transforming tabular data into a homogeneous form of a discrete digital signal. This paper proposes Streaming Super Tabular Machine Learning (SSTML), thereby exploring for the first time the potential of MDE in the difficult data stream classification task. SSTML encodes consecutive data chunks into an image representation using the STML algorithm and then performs a single ResNet-18 training epoch. Experiments conducted on synthetic and real data streams have demonstrated the ability of SSTML to achieve classification quality statistically significantly superior to state-of-the-art algorithms while maintaining comparable processing time. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Glucose Sensing Utilizing Complex-Valued Neural networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

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4th International Conference on Computing, networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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Increasing Depth of Neural networks for Life-Long Learning

SSRN

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SSRN 2023年

作者： Wozniak, Michal Kozal, Jedrzej Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeze Wyspianskiego 27 Wroclaw50-370 Poland

PurposeWe propose a novel method for continual learning based on the increasing depth of neural networks. This work explores whether extending neural network depth may be beneficial in a life-long learning *** propose a novel approach based on adding new layers on top of existing ones to enable the forward transfer of knowledge and adapting previously learned representations. We employ a method of determining the most similar tasks for selecting the best location in our network to add new nodes with trainable parameters. This approach allows for creating a treelike model, where each node is a set of neural network parameters dedicated to a specific task. The Progressive Neural Network concept inspires the proposed method. Therefore, it benefits from dynamic changes in network structure. However, Progressive Neural Network allocates a lot of memory for the whole network structure during the learning process. The proposed method alleviates this by adding only part of a network for a new task and utilizing a subset of previously trained weights. At the same time, we may retain the benefit of PNN, such as no forgetting guaranteed by design, without needing a memory *** on Split CIFAR and Split Tiny ImageNet show that the proposed algorithm is on par with other continual learning methods. In a more challenging setup with a single computer vision dataset as a separate task, our method outperforms Experience *** is compatible with commonly used computer vision architectures and does not require a custom network structure. As an adaptation to changing data distribution is made by expanding the architecture, there is no need to utilize a rehearsal buffer. For this reason, our method can be used for sensitive applications where data privacy must be considered. © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

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IEEE communications Surveys and Tutorials 2025年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Institute for Data Science and Computing University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada Shenzhen Research Institute of Big Data Future Network of Intelligence Institute The Chinese University of Hon g Kong School of Science and Engineering Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case 19

A Residual LSTM based Multi-Label Classification Framework f...

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19th IEEE Annual Consumer communications and Networking Conference, CCNC 2022

作者： Jalodia, Nikita Taneja, Mohit Davy, Alan Dezfouli, Behnam Waterford Institute of Technology Walton Institute for Information and Communication Systems Science Department of Computing and Mathematics Waterford Ireland Sfi Connect Research Centre for Future Networks and Communications Ireland Santa Clara University Department of Computer Science and Engineering Santa ClaraCA United States

ISBN: (纸本)9781665431620

We are witnessing an emergence of a new era of applications delivered via a paradigm of flexible and softwarized communication networks. This has opened the market to a wider movement towards virtualized applications and services in key verticals such as automated vehicles, smart grid, virtual reality (VR), Internet of Things (IoT), industry 4.0, telecommunications, etc. With an increasing emergence of verticals driven by the vision of low latency and high reliability, there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient in the long run. In this work, we present a Residual Long Short-Term Memory (LSTM) based multi-label classification framework for proactive SLA management in a latency-critical Network Function Virtualization (NFV) application use case. We compose a multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, and associate a multi-label classifier for a granular prediction of individual Service Level Objective (SLO) violations for each step in the forecast horizon. The Residual LSTM approach achieves an improvement of 31.1% over the baseline on the forecast classification accuracy, and a 2.65% improvement on the interpolated average precision over the standard LSTM methodology. © 2022 IEEE.

关键词： Long short-term memory

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Design and Implementation of an active safety system for Vehicular Ad-Hoc networks(VANETs)

Design and Implementation of an active safety system for Veh...

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International Conference on Information Networking

作者： Halimjon Hujamatov Debasis Das Amir Lazarev Ernazar Reypnazarov Doston Khasaniv Ankur Nahar Data Communication Networks and Systems Department Tashkent University of Information Technologies Named After Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Computer Science and Engineering Indian Institite of Technology Jodhpur Rajasthan India

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Vehicular communication, underpinned by IEEE 802.11p/WAVE-based Vehicle Ad-hoc networks (VANETs), is instrumental in the seamless functioning of intra-vehicle exchanges. However, a comprehensive assessment of these systems reveals suboptimal efficiencies at the data layer, specifically regarding default broadcast intervals. Such inefficiencies lead to escalated packet collisions and subpar utilization of the delay time counter—factors that undermine the synergistic interplay between Active Safety systems (ASS), such as Adaptive Cruise Control (ACC), and their passive safety counterparts. To address these intricacies, this research proposes an innovative mathematical framework tailored for the IEEE 802.11p MAC layer. We propose a model that elucidates the intricate dynamics of the delay time counter and offers refined broadcast intervals buttressed by robust algorithmic strategies. Empirical evaluations, conducted in meticulously simulated vehicular environments, validate the prowess of the proposed paradigm, highlighting a decline in packet collision instances. Quantitative findings from this research evince a notable decrease in packet collision rates and a commensurate enhancement in communication reliability, pivotal for advanced vehicular systems. Such technical augmentations directly elevate the operational reliability of cutting-edge safety mechanisms, exemplified by systems like the Toyota Pre-Crash Safety System.

关键词： Heuristic algorithms Instruments Vehicular ad hoc networks Physical layer Road safety Mathematical models Safety

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