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Australasian Telecommunication networks and Applications Conference, ATNAC

作者： Filippos Christou Andreas Kirstädter Institute of Communication Networks and Computer Engineering (IKR) University of Stuttgart Stuttgart Germany

The global internet relies on a well-coordinated operation of distinct networking domains. This renders multi-domain networking at the heart of today's massive digital information exchange. Although Software-Defined Networking (SDN) undoubtedly helps advance network operation within a single organization, non-centralized multi-domain networking has received less attention. To significantly advance the state of networking, we are inherently bound to provide progress and evolve the current multi-domain networking scheme. This work exploits the Intent-Based Networking (IBN) paradigm and the Directed Acyclic Graphs (DAGs) data structure to design a novel architecture for multi-domain IP-Optical networking. We highlight the benefits of our approach leading to seamless operation of non-centralized networks, such as optically transparent domain boundaries and cross-domain grooming. We evaluate this approach in a realistic scenario using our novel open-source tool ***, which we shortly introduce and can be broadly used for related research.

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Availability Estimation of Optical Network Links Using Multilevel Bayesian Modeling

Availability Estimation of Optical Network Links Using Multi...

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International Conference on Optical Network Design and Modeling, ONDM

作者： Filippos Christou Institute of Communication Networks and Computer Engineering (IKR) University of Stuttgart Stuttgart Germany

Estimating availability in transport networks is essential to successfully provision connectivity services under various requirements. As modern Service Level Agreements (SLAs) are becoming more demanding, it is crucial to estimate the true availability of an end-to-end path as soon and reliably as possible. Network operators might have expert knowledge regarding the theoretical availability of the network components, or they might have some measured data to infer such information. However, in either case, the true steady-state availability is unknown and must be inferred as a combination of the above. Traditionally, the estimation of Quality of Service (QoS) metrics, like availability, is done by producing a scalar value as an approximation. This can often be misleading and an oversimplification. In this paper, we build a multilevel Bayesian model that provides a probabilistic estimate of all network links’ availability by exploiting expert knowledge and the underlying data. The value of this methodology is greater for scenarios with scarce data, where the inference quality demonstrates remarkable stability regardless of the randomness of the incoming data. We demonstrate the effectiveness of our approach through simulation experiments and compare our results with the empirical interval availability. Our work has important implications for the design and management of optical networks since it constitutes a valuable tool that provides accurate availability estimations, which can be used for sophisticated decision-making.

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Security in Intent-Based Networking: Challenges and Solutions

Security in Intent-Based Networking: Challenges and Solution...

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IEEE Conference on Standards for communications and Networking (CSCN)

作者： Ijaz Ahmad Jere Malinen Filippos Christou Pawani Porambage Andreas Kirstädter Jani Suomalainen VTT Technical Research Centre of Finland Institute of Communication Networks and Computer Engineering (IKR) University of Stuttgart Germany

Intent-Based Networking (IBN) aims to automate administrative and management tasks in future communications networks. By leveraging networking concepts such as network abstractions and data-plane programmability and using artificial intelligence (AI), IBN improves the overall efficiency of communications networks. IBN employs a closed-loop architecture to monitor and optimize real-time network performance, reduce human intervention, and enhance resilience. However, the paradigm and the technological enablers introduce security challenges. This article studies the security gains and challenges in IBN from the aspect of enabling concepts and technologies. Furthermore, the article highlights potential solutions to existing challenges, outlines the standardization efforts, and summarizes the most important research gaps to advance future research in this direction.

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

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SN computer Science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Decentralized Intent-driven Coordination of Multi-Domain IP-Optical networks

Decentralized Intent-driven Coordination of Multi-Domain IP-...

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International Conference on Network and Service Management

作者： Filippos Christou Institute of Communication Networks and Computer Engineering (IKR) University of Stuttgart Stuttgart Germany

ISBN: (数字)9783903176515

ISBN: (纸本)9781665489928

Intent-based networking is increasingly used to improve network control and management. Network operators have already begun to adopt this paradigm, which leads to a simplified and automatized network operation. The operators can interact with their intent-driven networks through the Northbound Interface (NBI). Given a standardized NBI, the same approach can scale to coordinate intent provisioning across multi-domain networks in a decentralized fashion. This can outdate traditional decentralized protocols and open new opportunities for flexible and scalable communication mechanisms. This paper proposes a minimal and general high-level architecture, relying on a standard IBN (Intent-Based Networking) architecture, for multi-domain intent deployment in IP-optical networks. Our architecture is consistent between diverse network operators that use the same NBI, respects confidential information, promotes accountability, and can scale for various network services. To achieve this, we introduce a hierarchical system-generated intent schema with automatic intent delegation between the different domains.

关键词： Knowledge engineering Protocols Decentralized control Nonhomogeneous media Standards

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Queue-aware Network Control Algorithm with a High Quantum Computing Readiness—Evaluated in Discrete-time Flow Simulator for Fat-Pipe networks

Queue-aware Network Control Algorithm with a High Quantum Co...

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IEEE Workshop on High Performance Switching and Routing

作者： Arthur Witt Institute of Communication Networks and Computer Engineering University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350363852

ISBN: (纸本)9798350363869

The emerging technology of quantum computing has the potential to change the way how problems will be solved in the future. This work presents a centralized network control algorithm executable on already existing quantum computer which are based on the principle of quantum annealing like the D-Wave Advantage™. We introduce a resource reoccupation algorithm for traffic engineering in wide-area networks. The proposed optimization algorithm changes traffic steering and resource allocation in case of overloaded transceivers. Settings of active components like fiber amplifiers and transceivers are not changed for the reason of stability. This algorithm is beneficial in situations when the network traffic is fluctuating in time scales of seconds or spontaneous bursts occur. Further, we developed a discrete-time flow simulator to study the algorithm’s performance in wide-area networks. Our network simulator considers backlog and loss modeling of buffered transmission lines. Concurring flows are handled equally in case of a backlog. This work provides an ILP-based network configuring algorithm that is applicable on quantum annealing computers. We showcase, that traffic losses can be reduced significantly by a factor of 2 if a resource reoccupation algorithm is applied in a network with bursty traffic. As resources are used more efficiently by reoccupation in heavy load situations, overprovisioning of networks can be reduced. Thus, this new form of network operation leads toward a zero-margin network. We show that our newly introduced network simulator enables analyses of short-time effects like buffering within fat-pipe networks. As the calculation of network configurations in real-sized networks is typically time-consuming, quantum computing can enable the proposed network configuration algorithm for application in realsized wide-area networks.

关键词： Cloud computing Annealing Telecommunication traffic Switches Simulated annealing Quantum annealing Transceivers

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Queue-aware Network Control Algorithm with a High Quantum Computing Readiness—Evaluated in Discrete-time Flow Simulator for Fat-Pipe networks

arXiv

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arXiv 2024年

作者： Witt, Arthur Institute of Communication Networks and Computer Engineering University of Stuttgart Stuttgart Germany

关键词： Resource allocation

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Competition on robust deep learning

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National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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A low-fabrication-temperature, high-gain chip-scale waveguide amplifier

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Science China(Information Sciences) 2022年第6期65卷 247-255页

作者： Bo WANG Peiqi ZHOU Xingjun WANG Yandong HE State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer Science Peking University Institute of Microelectronics School of Electronics Engineering and Computer Science Peking University Frontier Science Center for Nano-optoelectronics Peking University Peking University Yangtze Delta Institute of Optoelectronics Peng Cheng Laboratory

The increasing prevalence of integrated on-chip optoelectronic devices has identified serious issues regarding inter-device transmission and coupling losses, highlighting an urgent need for on-chip waveguide amplifiers to compensate for these losses. Compared with other Er-based optical materials, erbium silicate is ideally suited to high-efficiency on-chip amplifiers and lasers because of its extremely high Er3+concentration(1022cm-3). Nevertheless, erbium silicate must be annealed above 1000℃ to crystallize and activate the Er3+, which damages other on-chip optoelectronic components and is not conducive to device ***, we report a low-fabrication-temperature, high-luminescence-efficiency gain material by adding Bi2O3to an erbium-ytterbium silicate mixed film. Our experiments demonstrate that the proposed film crystallizes at 600℃ while the activation of Er3+is also achieved, which is the lowest activation temperature of on-chip waveguide amplifier to our knowledge. This material forms the basis for a new chip-scale waveguide amplifier design, with a theoretical multi-energy-level model of Bi-Er-Yb in the mixed thin films used to analyze its signal enhancement properties. We achieve a peak on-chip gain of 23 dB in a 3.3-mm-long waveguide under the pump and signal powers of 300 mW and 1 μW, respectively. These results highlight the potential of the proposed material for realizing on-chip amplifiers and lasers for large-scale nanophotonic integrated circuits.

关键词： photonic circuit integration low fabrication temperature high gain rare earth material waveguide amplifier

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