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2nd International Workshop on Artificial Intelligence and Education, WAIE 2020

作者： Tao, J.U.N. Du, M.I.N. Wu, Y.U.E. Computer and Soft Engineer Department Anhui Institute of Information Technology China

ISBN: (纸本)9781450388252

IPv6 network is developing rapidly, so the teaching of IPv6 network becomes more important than ever, but IPv4 network and IPv6 network will coexist for a long time. So according to the coexistence of IPv4 and IPv6 networks, the experiment of IPv6 and IPv4 hybrid network is designed, IPv6 network transparently passes through IPv4 network through 6to4 tunnel, and IPv6 network router communicates through OSPFv3 Routing Protocol. The detailed design, implementation and complete verification of the experiment are given. Through the completion of this experiment, students have mastered the basic theory of IPv6 network, and they are familiar with the tunnel technology across heterogeneous networks, what's more, they can carry out simple IPv6 network design. © 2020 ACM.

关键词： Heterogeneous networks

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An Automated Solution for Securing Confidential Documents in a BYOD Environment 3

An Automated Solution for Securing Confidential Documents in...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Abisheka, P. A. C Azra, M. A. F Poobalan, A. V Wijekoon, Janaka Yapa, Kavinga Murthaja, Mifraz Sri Lanka Institute of Information Technology Department of Computer Systems and Engineering Sri Lanka WSO2 Senior Software Engineer Colombo Sri Lanka

ISBN: (纸本)9781665408622

BYOD or Bring Your Own Device is a set of policies that allow employees of an organization to use their own devices for official work purposes. BYOD is an immensely popular concept in the present day due to the many advantages it provides. However, the implementation of BYOD policies entail diverse problems and as a result, the confidentiality of documents can be breached. Furthermore, employees without security awareness and training are highly vulnerable to endpoint attacks, network attacks, and zero-day attacks that lead to a breach of confidentiality, integrity, and availability (CIA). In this context, this paper proposes a comprehensive solution;'BYODENCE', for the detection and prevention of unauthorized access to organizational documents. BYODENCE is an efficient BYOD solution which can produce competitive results in terms of accuracy and speed. © 2021 IEEE.

关键词： Zero-day attack

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms

Brain tumor classification model of ResNet-50 network based ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Menghan Zou Mingze Ma Anan Wang Yujie Li Teoh Teik Toe School of Computer Science and Technology Harbin Institute of Technology Weihai WeiHai China Welding Technology and Engineering Harbin Institute of Technology Weihai WeiHai China Marine Information Engineering Harbin Institute of Technology Weihai WeiHai China Information Management and Information System Harbin Institute of Technology Weihai WeiHai China NTU Business AI Lab Nanyang Technological University Singapore

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement.

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NIN-DSC: A Network Traffic Anomaly Detection Method Based on Deep Learning 7

NIN-DSC: A Network Traffic Anomaly Detection Method Based on...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Li, Yanmiao Kong, Xuan Hou, Jiangang Li, Xin Zhao, Kun Liang, Wei Jiang, Tongqing Xin, Yang Liu, Zhi Beijing University of Posts and Telecommunications Center of Information Science Beijing China Shandong University School of Information Science and Engineer Qingdao China Beijing Institute of Technology School of Computer Science & Technology Beijing China Inspur Electronic Information Industry Co. Ltd Jinan China Qilu University of Technology School of Information and Automation Jinan China Shandong Gentle Tecchnology Co. Ltd Jinan China Beijing University of Posts and Telecommunications Centre of Information Security Beijing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Network traffic anomaly detection plays an important role in maintaining network space security and is usually modeled as a traffic classification problem. With the rapid change of the Internet, the network traffic environment is increasingly complex, and the traditional classification method is no longer applicable. In this article, we propose a new traffic classification framework for network anomaly detection, which operates directly on the original traffic. The NIN-DSC network was used to extract the features of the data, and the validation was carried out in the public data set. © 2022 IEEE.

关键词： Deep neural networks

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Joint Relay and Mode Selection for Covert Communication in Wireless Relay systems

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IEEE Transactions on Communications 2024年

作者： Liu, Yan Wu, Huihui Su, Wei Shen, Yulong Jiang, Xiaohong Xidian University Hangzhou Institute of Technology Hangzhou311231 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Xidian University School of Computer Science and Technology Xi'an710071 China Future University Hakodate School of System Information Science Hakodate041-8655 Japan

This paper investigates the joint relay and transmission mode selection for covert communication in a wireless relay system with amplify-and-forward (AF) forwarding mode, which consists of one source, multiple AF relays, one destination, one friendly jammer and one warden, and each relay can switch between the half-duplex (HD) and full-duplex (FD) transmission modes. We first explore the fundamental covert performance of the system when it works in either the fixed HD or fixed FD mode. Based on this result, we then investigate the covert performance of the system with optimal (resp. random) relay selection and random (resp. optimal) mode selection, so as to reveal the achievable covert performance in the system with solely the relay selection or mode selection. Building upon above results, we further design the optimal joint relay and mode selection scheme, and develop related theoretical models for performance analysis. Finally, we provide extensive numerical results to conduct a comprehensive comparison between the relay selection and mode selection on their achievable covert performance and to illustrate the performance enhancement from adopting the joint relay and mode selection in the relay system. © 1972-2012 IEEE.

关键词： Electric relays

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Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps

arXiv

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arXiv 2024年

作者： Manan, Malik Abdul Jinchao, Feng Yaqub, Muhammad Ahmed, Shahzad Imran, Syed Muhammad Ali Chuhan, Imran Shabir Khan, Haroon Ahmed Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of computer science and information technology Superior University Lahore Pakistan Interdisciplinary Research Institute Faculty of Science Beijing University of Technology Beijing China Islamabad Pakistan

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer. © 2024, CC BY.

关键词： Semantic Segmentation

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Dynamic Digital Signage system: A Cost-Effective and Unified Web-Based Solution for Content and Analytics Management 2

Dynamic Digital Signage System: A Cost-Effective and Unified...

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2nd International Conference on Image Processing and Media Computing, ICIPMC 2023

作者： Cabading, Kriselyn Malaca, Orlando Juanatas, Ronaldo Tena, Policarpio Goh, Joselito Eduard Goh, Marie Luvett Juanatas, Irish Juanatas, Roben Technological University of the Philippines College of Engineering Manila Philippines Technological University of the Philippines College of Industrial Education Manila Philippines Technological University of the Philippines Graduate Program and External Studies Manila Philippines College of Information System de la Salle - College of Saint Benilde Manila Philippines Far Eastern University - Institute of Technology College of Computer Studies and Multimedia Arts IT Department Manila Philippines Far Eastern University Information Technology Department Quezon City Philippines National University Philippines College of Computing and Information Technology Manila Philippines

ISBN: (纸本)9798350326611

While several content management systems (CMS) and audience analytics tools are available for digital signage in the market, they are often sold separately and can be expensive. Therefore, this project aims to design a cost-effective and unified web-based solution for digital signage that combines content management and audience analytics functions, reducing the need for multiple purchases. This can be achieved by utilizing Raspberry Pi technology, known for its cost-effectiveness and versatility in integrations, along with a face recognition camera and machine learning methods. This proof of concept demonstrates the integration of these components to create a dynamic digital signage system. Overall, this project has the potential to offer an affordable solution for companies aiming to efficiently manage and optimize their digital marketing strategies, especially in the Digital Out-of-Home (DOOH) Advertising space. © 2023 IEEE.

关键词： Websites

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Sensor Infused Emperor Penguin Optimized Deep Maxout Network for Paralyzed Person Monitoring

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IEEE Sensors Journal 2024年

作者： Yang, Jing Hu, Ruoyang Wu, Chenwei Jiang, Gaozhe Alkanhel, Reem Ibrahim Elmannai, Hela Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Boston University Sargent College BostonMA02115 United States Evident Battery Inc MA02138 United States National University of Singapore Singapore Institute of Operations Research and Analytics Singapore119007 Singapore Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia

Paralyzed people must maintain proper posture to prevent respiratory problems, pressure sores, and muscular contractures. A paralyzed person may slip out of their wheelchair several times as a result of poor sitting position. To overcome these challenges, a novel Emperor Penguin optimized sensor Infused wheelChair (EPIC) framework has been proposed to constantly monitor the real-time posture of the paralyzed person's health state. The proposed model aims to monitor wheelchair posture and health in real-time, which can automatically detect posture issues, providing timely alerts and feedback to the user via a mobile application. The proposed framework utilizes the Emperor Penguin Optimizer (EPO) algorithm for feature selection to improve the accuracy of posture detection. A Deep Maxout Network (DMN) analyzes the features to predict the posture of the wheelchair-bound patient. The proposed EPIC framework has been assessed using a Python simulator. The effectiveness of the proposed EPIC framework has been determined using evaluation metrics such as precision, specificity, accuracy, and sensitivity. The proposed EPIC technique advances the overall accuracy by 10.1%, 7.73%, and 2.84% better than posture recognition, Graphic User Interface (GUI), and Independent Component Analysis-Kantorovich Distance (ICA-KD) respectively. © 2024 IEEE.

关键词： Graphical user interfaces

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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Zoological Research 2023年第5期44卷 967-980页

作者： Chuxi Li Zifan Xiao Yerong Li Zhinan Chen Xun Ji Yiqun Liu Shufei Feng Zhen Zhang Kaiming Zhang Jianfeng Feng Trevor W.Robbins Shisheng Xiong Yongchang Chen Xiao Xiao School of Information Science and Technology Micro Nano System Center Fudan UniversityShanghai 200433China Department of Anesthesiology Huashan HospitalKey Laboratory of Computational Neuroscience and Brain-Inspired IntelligenceMinistry of EducationBehavioral and Cognitive Neuroscience CenterInstitute of Science and Technology for Brain-Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Kuang Yaming Honors School Nanjing UniversityNanjingJiangsu 210023China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan 650500China New Vision World LLC. Aliso ViejoCalifornia 92656USA Behavioural and Clinical Neuroscience Institute University of CambridgeCambridgeCB21TNUK

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and ***,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized *** this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton ***,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in *** the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual *** Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

关键词： Action recognition Fine motor identification Two-stream deep model 2D skeleton Non-human primates Rett syndrome

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