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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Development of a Voice-Enabled Automated Sleep Survey System

Development of a Voice-Enabled Automated Sleep Survey System

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yang Qiu Anwesh Mohanty Robert L. Owens Shamim Nemati Computer Science and Engineering University of California San Diego La Jolla USA Department of Biomedical Informatics University of California San Diego La Jolla USA Department of Medicine University of California San Diego La Jolla USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

To enhance sleep quality in hospitalized patients, we developed a conversational agent that streamlines the collection and analysis of sleep data. The system employs the Richards-Campbell Sleep Questionnaire, supplemented by additional questions about environmental factors such as room temperature and lighting, to comprehensively evaluate sleep disturbances experienced by patients. By processing patients’ spoken responses, the agent identifies environmental and care-related factors impacting sleep, which may allow for non-pharmacological interventions to enhance sleep quality. The integration of advanced conversational AI technologies, including GPT-4 and large language models, is a key feature of this system, enabling nuanced interpretation and structuring of patient feedback. This approach not only streamlines sleep assessment in hospital settings but also aligns with the shift towards patient-centric healthcare. By offering detailed insights into factors affecting sleep, the system showcases its potential with a high recognition accuracy, underscoring its potentially valuable role in advancing healthcare sleep quality management.

关键词： Surveys Accuracy Temperature Hospitals Large language models Lighting Focusing Environmental factors Text to speech Quality assessment

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Detecting Clickbait in Chinese Social Media by Prompt Learning

Detecting Clickbait in Chinese Social Media by Prompt Learni...

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International Conference on computer Supported Cooperative Work in Design

作者： Yin Wu Mingpei Cao Yueze Zhang Yong Jiang Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Informatics Technical University of Munich Munich Germany

Online social media has evolved into an essential collaboration system for information exchange. Clickbait refers to sentences containing tricks to attract users to click for commercial profit. In this work, we propose Part-of-speech Enhanced Prompt Learning (PEPL) method for clickbait detection. Our method introduce prompt learning into clickbait detection to stimulate the rich knowledge in pre-trained language models by adding task-specific guidance. Additionally, we design a part-of-speech enhanced prompt to realize grammatical features guided semantic understanding. We conduct extensive experiments to evaluate the performance. The results indicate that our method performs best in low-resource and full-scale scenarios and is still effective in extreme few-shot settings. We also perform ablation and generalization studies to demonstrate the necessity of the proposed components and that our method could be applied to enhance other pre-trained language models. Our method can be adopted in social media and assist downstream tasks to achieve better user-friendly collaboration systems.

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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CLEO: Applications and Technology, CLEO:A and T 2023 - Part of Conference on Lasers and Electro-Optics 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. Boltasseva, Alexandra School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

ISBN: (纸本)9781957171258

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 µm for macroscopic color printing by utilizing femtosecond laser photomodification of multi-functional optical elements. CLEO 2023 © Optica Publishing Group 2023, © 2023 The Author(s)

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Raeed AL-Sabri Jianliang Gao Jiamin Chen Babatounde Moctard Oloulade Zhenpeng Wu Monir Abdullah Xiaohua Hu School of Computer Science and Engineering Central South University Changsha China Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khlifa University Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.

关键词： Representation learning Biological system modeling Manuals Predictive models Neural architecture search Topology Bioinformatics Faces

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Linear-time Computation of DAWGs, Symmetric Indexing Structures, and MAWs for Integer Alphabets

arXiv

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arXiv 2023年

作者： Fujishige, Yuta Tsujimaru, Yuki Inenaga, Shunsuke Bannai, Hideo Takeda, Masayuki Department of Informatics Kyushu University Japan Department of Electrical Engineering and Computer Science Kyushu University Japan M&D Data Science Center Tokyo Medical and Dental University Japan

The directed acyclic word graph (DAWG) of a string y of length n is the smallest (partial) DFA which recognizes all suffixes of y with only O(n) nodes and edges. In this paper, we show how to construct the DAWG for the input string y from the suffix tree for y, in O(n) time for integer alphabets of polynomial size in n. In so doing, we first describe a folklore algorithm which, given the suffix tree for y, constructs the DAWG for the reversed string ŷ in O(n) time. Then, we present our algorithm that builds the DAWG for y in O(n) time for integer alphabets, from the suffix tree for y. We also show that a straightforward modification to our DAWG construction algorithm leads to the first O(n)-time algorithm for constructing the affix tree of a given string y over an integer alphabet. Affix trees are a text indexing structure supporting bidirectional pattern searches. We then discuss how our constructions can lead to linear-time algorithms for building other text indexing structures, such as linear-size suffix tries and symmetric CDAWGs in linear time in the case of integer alphabets. As a further application to our O(n)-time DAWG construction algorithm, we show that the set MAW(y) of all minimal absent words (MAWs) of y can be computed in optimal, input- and output-sensitive O(n + |MAW(y)|) time and O(n) working space for integer alphabets. © 2023, CC BY.

关键词： Indexing (of information)

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Quality Grading of Beef using Deep Convolutional Neural Network 9

Quality Grading of Beef using Deep Convolutional Neural Netw...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Sharma, Aarav Choudhury, Tanupriya Sarkar, Tanmay Mohanty, Madhusmita Bansal, Nikunj Mohanty, Sachi Nandan Informatics Cluster School of Computer Science Uttarakhand Dehradun2480007 India Cse Dept. Uttarakhand Dehradun India Graphic Era Hill University Cse Dept. Uttarakhand Dehradun248002 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Department of Food Processing Technology Malda732102 India Andhra Pradesh Amaravati India VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati5222237 India

ISBN: (纸本)9798350327502

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy. © 2023 IEEE.

关键词： computer vision

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment 6

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Zariful, Wafiq Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Basri, Aida Maryam Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Fakhrurroja, Hanif Universiti Teknologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei Universiti Teknologi Brunei School of Applied Sciences and Mathematics Bandar Seri Begawan Brunei Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Johor Bahru Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Telkom University Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350317428

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications. © 2023 IEEE.

关键词： Object detection

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Improved detection mechanism in MANETs using adaptive IP tracing mechanism

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International Journal of Intelligent Networks 2020年 1卷 102-106页

作者： Mamade, Bayisa Kune Dhas, C. Suresh Gnana School of Informatics and Electrical Engineering Institute of Technology Ambo University Ethiopia Department of Computer Science Institute of Technology Ambo University Ethiopia

The network has enabled enormous volumes of services without any restrictions, different users need to have access to the service provided are more focused by malicious users. It is imperative to identify malicious entry or access to services, hence this paper discusses the various aspects of malicious access due to DDoS attacks and on how to detect it also this method uses the decisions made by hackers to manage the Traceback information science. In this paper, we consider the flash crowds based on the difference between DDoS attacks, a flash crowd is used to mitigate the DDoS attack. There is a tendency to see information on flux entropy variations on routers’ short-term and store them. Once the victim has been aware of a DDoS attack, the victim starts pushback tracing. The initial Traceback rule identifies its upstream routers where attacks have taken place and transmits the requests for Traceback to the upstream connected routers. If the flow rate is low, the low detection rule is initiated to trace the attackers and therefore the study is evaluated based on the instances where the packets are attacked by malicious users. The evaluation is conducted based on whether the packet traversed is malicious or normal and finally the accuracy of IP Traceback techniques and time complexity are tested. © 2020 The Author(s)

关键词： Entropy

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Model-Based Rate-Distortion Optimized Video-Based Point Cloud Compression with Differential Evolution 11th

Model-Based Rate-Distortion Optimized Video-Based Point Clou...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Yuan, Hui Hamzaoui, Raouf Neri, Ferrante Yang, Shengxiang School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Control Science and Engineering Shandong University Jinan China School of Computer Science University of Nottingham Nottingham United Kingdom School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9783030873547

The Moving Picture Experts Group (MPEG) video-based point cloud compression (V-PCC) standard encodes a dynamic point cloud by first converting it into one geometry video and one color video and then using a video coder to compress the two video sequences. We first propose analytical models for the distortion and bitrate of the V-PCC reference software, where the models’ variables are the quantization step sizes used in the encoding of the geometry and color videos. Unlike previous work, our analytical models are functions of the quantization step sizes of all frames in a group of frames. Then, we use our models and an implementation of the differential evolution algorithm to efficiently minimize the distortion subject to a constraint on the bitrate. Experimental results on six dynamic point clouds show that, compared to the state-of-the-art, our method achieves an encoding with a smaller error to the target bitrate (4.65% vs. 11.94% on average) and a slightly lower rate-distortion performance (on average, the increase in Bjøntegaard delta (BD) distortion is 0.27, and the increase in BD rate is 8.40%). © 2021, Springer Nature Switzerland AG.

关键词： Encoding (symbols)

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