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An Optimal Framework for SDN Based on Deep Neural Network

computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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Evaluating Fast Adaptability of Neural Networks for Brain-computer Interface

arXiv

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arXiv 2024年

作者： Sharma, Anupam Miyapuram, Krishna Department of Computer Science & Engineering Indian Institute of Technology Gujarat Gandhinagar India Department of Cognitive & Brain Sciences Indian Institute of Technology Gujarat Gandhinagar India

Electroencephalography (EEG) classification is a versatile and portable technique for building non-invasive Brain-computer Interfaces (BCI). However, the classifiers that decode cognitive states from EEG brain data perform poorly when tested on newer domains, such as tasks or individuals absent during model training. Researchers have recently used complex strategies like Model-agnostic meta-learning (MAML) for domain adaptation. Nevertheless, there is a need for an evaluation strategy to evaluate the fast adaptability of the models, as this characteristic is essential for real-life BCI applications for quick calibration. We used motor movement and imaginary signals as input to Convolutional Neural Networks (CNN) based classifier for the experiments. Datasets with EEG signals typically have fewer examples and higher time resolution. Even though batch-normalization is preferred for Convolutional Neural Networks (CNN), we empirically show that layer-normalization can improve the adaptability of CNN-based EEG classifiers with not more than ten fine-tuning steps. In summary, the present work (i) proposes a simple strategy to evaluate fast adaptability, and (ii) empirically demonstrate fast adaptability across individuals as well as across tasks with simple transfer learning as compared to MAML approach. © 2024, CC BY.

关键词： Electroencephalography

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Chiral exceptional point enhanced active tuning and non reciprocity in micro-resonators

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光：科学与应用（英文版） 2025年第2期14卷 401-410页

作者： Hwaseob Lee Lorry Chang Ali Kecebas Dun Mao Yahui Xiao Tiantian Li Andrea Alù Sahin K.Özdemir Tingyi Gu Department of Electrical and Computer Engineering University of DelawareNewarkDelaware 19716USA Department of Engineering Science and Mechanics Pennsylvania State UniversityUniversity ParkPA 16802USA Photonics Initiative Advanced Science Research CenterCity University of New YorkNew YorkNY 10031USA Physics Program Graduate CenterCity University of New YorkNew YorkNY 10016USA Department of Engineering Science and Mechanics Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Computer Engineering Saint Louis UniversitySaint LouisMO 63103USA

Exceptional points(EPs)have been extensively explored in mechanical,acoustic,plasmonic,and photonic ***,little is known about the role of EPs in tailoring the dynamic tunability of optical devices.A specific type of EPs known as chiral EPs has recently attracted much attention for controlling the flow of light and for building sensors with better responsivity.A recently demonstrated route to chiral EPs via lithographically defined symmetric Mie scatterers on the rim of resonators has not only provided the much-needed mechanical stability for studying chiral EPs,but also helped reduce losses originating from nanofabrication imperfections,facilitating the in-situ study of chiral EPs and their contribution to the dynamics and tunability of ***,we use asymmetric Mie scatterers to break the rotational symmetry of a microresonator,to demonstrate deterministic thermal tuning across a chiral EP,and to demonstrate EP-mediated chiral optical nonlinear response and efficient electro-optic *** results indicate asymmetric electro-optic modulation with up to 17 dB contrast at GHz and CMOS-compatible voltage *** wafer-scale nano-manufacturing of chiral electro-optic modulators and the chiral EP-tailored tunning may facilitate new micro-resonator functionalities in quantum information processing,electromagnetic wave control,and optical interconnects.

关键词： active point micro chiral enhanced exceptional reciprocity resonators tuning

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DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-ENABLED WIRELESS COMMUNICATIONS SYSTEM

DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-...

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2023 International Conference on Innovation, Communication and engineering, ICICE 2023

作者： Tesfaw, Belayneh A. Lin, Hsin-Piao Tarekegn, Getaneh B. Juang, Rong-Terng Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications. © The Institution of engineering & Technology 2023.

关键词： Channel estimation

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Hidayaturrahman Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusatara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusatara University Jakarta Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.

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Robust Frame-level Speaker Localization in Reverberant and Noisy Environments by Exploiting Phase Difference Losses

Robust Frame-level Speaker Localization in Reverberant and N...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shanmukha Srinivas Battula Hassan Taherian Ashutosh Pandey Daniel Wong Buye Xu DeLiang Wang Department of Computer Science and Engineering The Ohio State University USA Meta Reality Labs USA Center for Cognitive and Brain Sciences The Ohio State University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates robust speaker localization at the frame level on the basis of complex spectral mapping, which is capable of learning both the magnitude and phase of the target signal. Unlike prevailing deep learning methods for speaker localization, we perform MIMO (multi-input multi-output) based multi-channel speech enhancement first and then localize the enhanced speaker using weighted generalized cross correlation. In addition, we propose new multi-channel loss functions that incorporate phase differences in order to preserve inter-channel phase relations, which is key to accurate sound localization. Systematic evaluations using simulated and recorded room impulse responses demonstrate that the proposed model yields excellent frame-level speaker localization results in reverberant and noisy environments and outperforms related methods by a large margin, even surpassing their utterance-level results.

关键词： Location awareness Deep learning Accuracy Systematics Correlation Speech enhancement Signal processing Acoustics Noise measurement

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Purchase intention of Amazon sustainable brands: a hybrid approach of structural equation modeling and Rasch model

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SN Business and Economics 2023年第12期3卷 1-29页

作者： Santos, Luiz Diego Vidal Holanda, Francisco Sandro Rodrigues de Santana Oliveira, Catuxe Varjão Pedrotti, Alceu de Menezes, Pedro Vinícius Bertulino Villwock, Ana Paula Schervinski Graduate Program in Intellectual Property Science Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Agronomy Engineering Department Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Computer Science Department Instituto Federal de Sergipe-UFS Sergipe Lagarto Brazil

This article conducts a comprehensive examination of brand-based equity closely linked to environmental sustainability in the Amazon Rainforest. The research is primarily dedicated to identifying the factors that influence consumers’ willingness to purchase products from sustainable brands within six categories: cosmetics, tourism, clothing, accessories, and food. The central aim of this study is to investigate how brand equity constructs, rooted in environmental sustainability within the Amazon Rainforest, impact consumer loyalty and purchase intention. To accomplish this objective, a mixed-methodology approach was employed, combining structural equation modeling with item response theory. This study involved 535 potential consumers, and data from eight selected brands served as the basis for the analysis. Within the framework of Purchase Intention (PI), the following variables were regarded as formative constructs: Strategic Brand Positioning (SBP), Perceived Brand Quality (PBQ), Retail Innovation (RI), and Brand Authenticity (BA). The results obtained from the structural equation models underscore the statistical significance and substantial impact of all pathways. Notably, SBP emerged as the most significant contributor to the effect on PI, as hypothesized. Brand equity and environmental sustainability were found to indirectly affect behavioral PI. Consumers exhibited a high level of latent trait (θ), with items consistently reflecting elevated values, confirming their acceptance of products, except brand differentiation. Overall, the findings highlight the effectiveness of branding strategies rooted in environmental sustainability in enhancing the brand equity of small forest companies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Amazon Rainforest Brands Entrepreneurship Microenterprises Sustainability performance Sustainable development

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The Effect of Viewpoint Change Strategies on Multi-View Video and Audio Transmission QoE Over ICN/CCN

The Effect of Viewpoint Change Strategies on Multi-View Vide...

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IEEE Region 10 International Conference TENCON

作者： Toshiro Nunome Yoshiyuki Takada Department of En-gineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technol-ogy Nagoya Japan

In this paper, we exploit caches on intermediate nodes for QoE enhancement of multi-view video and audio transmission over ICN/CCN by controlling the content request start timing of consumers. We assume the selected single viewpoint transmission method; a consumer receives video and audio streams of a requested viewpoint. We perform a simple experiment with two consumers. When the consumers play video and audio with the time difference, we assess the effect of cached content by the former consumer's request on the output quality of the latter consumer. We deal with two types of viewpoint change strategies for the former consumer, which affect the efficiency of cache utilization. From the assessment results, we see that cache utilization has an important factor in enhancing QoE.

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Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

ISBN: (纸本)9781957171395

关键词： X ray lasers

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Using Deep Learning Models for COVID-19 Related Sentiment Analysis on Twitter Data

Using Deep Learning Models for COVID-19 Related Sentiment An...

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Human-Centered cognitive Systems (HCCS)

作者： Waqas Haider Bangyal Saeeda Amina Rabia Shakir George Ubakanma Muddesar Iqbal Department of Computer Science Kohsar University Murree (KUM) Murree Pakistan Department of Computer Science University of Sialkot (USKT) Sialkot Pakistan Department of Computer Science FUUAST Islamabad Pakistan Cognitive Systems Research Centre School of Engineering School of Engineering London South Bank University

The significance of sentiment analysis has increased in modern times due to the extensive use of social media platforms as a medium for individuals to express their opinions. Twitter is widely acknowledged as a popular social media site mostly used for microblogging. People often voice their opinions on current events, making it challenging for researchers to accurately classify the mood expressed in these opinions. This research study presents a novel and efficient method for identifying and detecting false or misleading information pertaining to the COVID-19 pandemic. The dataset including artificially created news stories is obtained from a collection of texts and processed via the cycle of natural language processing (NLP). For this research study, three advanced deep learning models were used to predict the emotion of news items, accurately differentiating between genuine and fraudulent ones. This study utilizes Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers. Subsequently, it compares the outcomes achieved from these classifiers. The findings suggest that the BiGRU deep learning classifier has exceptional accuracy and efficiency, achieving a remarkable accuracy rate of 91%.

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