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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Matthias Frei Reinhard German Anatoli Djanatliev Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Dept. of Computer Science Computer Networks and Communication Systems University of Erlangen-Nürnberg Germany

Future networks with well-developed Multi-Access Edge Computing (MEC) resources, where network nodes and distributed service instances can form a dense topology, face the challenge of distributing service data among many nodes. In particular, frequent data changes require an efficient update mechanism. System flexibility and user mobility, e.g, cellular hand-overs, or unpredictable service deployment or node changes, pose additional challenges. In this paper, a method is proposed and evaluated, how network graph-structured data can be distributed and synchronized between many nodes. The design considers especially the challenges of mobile networks with dynamic changes. At the example of a MEC scenario, the method is evaluated using a system-level simulation environment with fifth generation (5G) cellular communication, mobile as well as locally bound nodes, and dynamic service deployment changes. Network and computing resources are represented and shared using a graph. To gain deeper insight into the performance of our model, the time and bandwidth resources required to replicate graph updates to all clients in the 5G network are evaluated. Hereby, the impact of different background network traffic, of information-dependent graph update frequencies, and of the connectivity type can be shown. Finally, by implementing a practical example, the maintenance overhead for synchronizing the graph data to have a local information source is compared to the time required when querying remote information sources.

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Prediction-Assisted Compressed Spectrum Sensing

Prediction-Assisted Compressed Spectrum Sensing

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2023 IEEE/CIC International Conference on communications in China, ICCC 2023

作者： Song, Zihang Yang, Jian Zhang, Han Peng, Jinbo Gao, Yue The Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom The China Academy of Information and Communications Technology Beijing100191 China Fudan University The School of Computer Science Shanghai200433 China

ISBN: (纸本)9798350345384

In compressed spectrum sensing (CSS), true spectrum support is often assumed to be entirely unknown, as is the case in real-world applications. This unknown spectrum support, however, renders the design of the sensing matrix a complex and arduous task. In this paper, we demonstrate that it is possible to obtain certain prior knowledge on the spectrum support, which can then be employed to efficiently design a high-quality sensing matrix. We propose prediction-assisted CSS schemes that incorporate integrated spectrum support prediction methods or rely on external cooperative decisions to update the sensing matrix in accordance with spectrum dynamics. Numerical experiments reveal that the proposed schemes yield significant performance improvements in both single-node and cooperative scenarios. © 2023 IEEE.

关键词： Forecasting

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

Wavelength-division-multiplexed Transmission and Dispersion ...

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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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Surface Sampling, Vertex Manipulation and Surface Generation Based on WebGL Technologies 7

Surface Sampling, Vertex Manipulation and Surface Generation...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Runchevski, Danail Rechkoska-Shikoska, Ustijana Salkoski, Rasim University for Information Science and Technology 'St. Paul the Apostle' Faculty of Communication Networks and Security Resen Macedonia University for Information Science and Technology 'St. Paul the Apostle' Faculty of Computer Science and Engineering Ohrid Macedonia University for Information Science and Technology 'St. Paul the Apostle' Faculty of Information Systems Visualisation Multimedia and Animation Ohrid Macedonia

ISBN: (纸本)9798350342154

Surface sampling is a powerful technique used in computer graphics and visualization to gather data from 3D surfaces. Surface sampling is a process of collecting visual and tactile information from real world surfaces to serve as references for artistic creations. It allows artists to study and analyze the intricate details of surfaces, such as the roughness of tree bark, the smoothness of a pebble, or the subtle variations in fabric patterns. By examining surfaces up close, artists gain insights into the nuances that make each surface visually and texturally distinct. The solution we've developed operates seamlessly on both desktop and mobile devices, utilizing the functionalities of widely used web browsers. It eliminates the need for any software installations. Our achievement is founded on fully open-source solutions, allowing users to explore 3D models without any barriers. © 2023 IEEE.

关键词： Web browsers

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Realization and Performance Tests of a Dual-Polarization Fiber Optic Gyroscope 8

Realization and Performance Tests of a Dual-Polarization Fib...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Zhou, Tong Zhu, Lanxin Cao, Yuwen Chen, Yanjun Huang, Huimin Li, Zhengbin School of Software and Microelectronics Peking University Beijing102600 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Interferometric fiber optical gyroscopes (IFOGs) have become one of the widely used sensors of inertial technology and rotational seismology, owing to their high precision and stability. In recent years, the dual-polarization IFOG, which allows two orthogonal polarization to work simultaneously, has been proved to achieve better measurement performance than the conventional minimum configuration through mutual compensation. In this work, a prototype of dual-polarization fiber-optic gyroscope is designed and realized. Laboratory tests show the self-noise has reached 9 nrad/s/sqrt(Hz) over the frequency from 0.01 to 125 Hz and the angular random walk has achieved 2.6×10-5 deg/sqrt(hr). For an Open-loop IFOG, which does not need a confiscated feedback control system, it avoids the dead-zone of closed-loop IFOG. However, limited by the measurement range, as a result of the periodic and nonlinear response of open-loop demodulation, the scale factor of open-loop IFOG varies around 100~1000 parts per million (ppm) conventionally, which hinders its detection accuracy. In this work, with new measurement extension and linearity compensation methods, the prototype achieves an outstanding precision with scale factor nonlinearity up to 5 ppm within ±10 deg/s, and shows the potential of dual-polarization IFOGs for rotational seismology. © 2022 SPIE

关键词： Seismology

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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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