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检索条件"任意字段=2nd International Conference on Computer Science and Software Engineering, CSASE 2022"

共 8419 条记录，以下是51-60 订阅

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Research on Distributed Optical Fiber Acoustic Sensing Technology for Hydroacoustic Demodulation 2

Research on Distributed Optical Fiber Acoustic Sensing Techn...

引用

2nd IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2024

作者： Wang, Ziyi Liu, Hengrui Liu, Jiahao Zhang, Hanwen Yuan, Yibo Liang, Guangxin Jia, Zixiang Li, Jianyu Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350368208

In this paper, the principle of distributed optical fiber acoustic sensing (DAS) technology is briefly described, and the experiment of hydroacoustic signal demodulation and restoration by DAS technology is designed. It is verified that DAS technology can realize the linear and distortionless restoration of hydroacoustic signal at different frequencies and amplitudes. The calibration experiment is also designed to measure the acoustic pressure sensitivity, which shows that the acoustic pressure sensitivity of the system reaches-139.7dB. The results show that the DAS technology has a strong practical application effect and broad application prospects in the field of underwater acoustic signal monitoring. © 2024 IEEE.

关键词： Demodulation

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Analysis and Research on Learning Behaviors Based on K-Means Algorithm in MOOC Platforms 2

Analysis and Research on Learning Behaviors Based on K-Means...

引用

2nd IEEE international conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Chen, Jianqiang Software Engineering Institute of Guangzhou Guangzhou510900 China

ISBN: (纸本)9798350373646

Massive Open Online Courses (MOOC) is a fruitful attempt for online education to target the whole society and achieve comprehensive educational equity, serving as a powerful supplement and enrichment to offline education. Learners can access courses and knowledge from all over the world through online access at any time and any place. Taking the Web log analysis of the MOOC platform as the project background, this paper utilizes the ELK technology stack (Elasticsearch + Logstash + Kibana) to complete the collection, storage, and organization of learning behavior data on the MOOC platform. Furthermore, an online learning behavior clustering analysis method based on the k-means clustering algorithm is employed, providing significant data analysis and application support for online learning platforms. This approach assists teachers and platform managers in adjusting teaching modes and strategies in a timely manner to enhance learners' online learning effectiveness. © 2024 IEEE.

关键词： Federated learning

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A Multi-feature Co-distillation Algorithm Based on Human Skeletal Point Behavior Recognition 24

A Multi-feature Co-distillation Algorithm Based on Human Ske...

引用

2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Pan, Xuefeng Tian, Zhiqiang School of Computer Wuhan Qingchuan University Wuhan China School of Information Wuhan Software Engineering Vocational College Wuhan Open University Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400712883

Depth model-based behavior estimation of human skeletal points is widely used in the field of behavior recognition. In order to improve the accuracy of behavior recognition, the complexity and computation of the designed network model are increasing, while the mobile platforms have limited arithmetic power and memory, and thus cannot be deployed. To solve the above problems, the AGCN network, a skeletal point-based behavior recognition model, is clipped to compress the network size so that it can support common mobile platforms;and a multi-feature co-distillation method is proposed to efficiently migrate the performance of the original network to the target network, so that the performance of the clipped lightweight network tends to be close to that of the original network. The model training, testing, visualization, and ablation experiments are completed on the NTU-RGB+D60 data set. The experimental results show that the algorithm after lightweighting (clipping network and multi-feature co-distillation), can significantly reduce the computational amount of behavior recognition. © 2024 Copyright held by the owner/author(s).

关键词： Distillation

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Enhancing Multimedia Forwarding in software- Defined Networks: An Optimal Flow Mechanism Approach 2

Enhancing Multimedia Forwarding in Software- Defined Network...

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2nd international conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Tiwana, Pardeep Singh Singh, Jaspreet Chandigarh University Gharuan Computer Science & Engineering Punjab Mohali India Chandigarh Engineering College-CGC Landran Punjab Mohali India

ISBN: (纸本)9798350371314

software-defined networking (SDN) has revolutionized the way in which current modern networks are constructed. SDN offers dynamic and centralized administration resources that are available for the network, but multimedia traffic still a challenge on modern communication networks and it is very important to protect network systems that can reliably and effectively send multimedia data over the network form one end to another end. The goal of this new approach, which is called the Optimal Proposed Flow Mechanism (OPFM), is to make it easier for SDN (software-defined networking) devices to send multimedia data. This method of managing flow at the D-Plane involves the enforcement of device thresholds that are determined by the packet content of messages. By implementing this approach, not only are speed of response and computational efficiency improved, but processing delays are also reduced. The subsequent emphasis is on flow classification, which involves the categorization of incoming flows into delay-sensitive and nondelay-sensitive streams. A Non-Deep Lightweight Parallel Network (nd-LPN) is used for classification in software defined network and it has parallel layers that can manage flow delay limits, device efficiency, and service categorization. In the way to prevent packet loss while load balancing, this approach prioritizes flow classification in a computing efficient and latency-free manner. This means that traffic flow is sent to controllers according to their importance. The proposed method conducted in Network Simulator 3.2 and it makes the network work better by fixing flow management problems through more efficient methods for categorizing and transferring traffic in terms of network load and packet loss rate during flow forwardoing. © 2024 IEEE.

关键词： software defined networking

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Empowering Cyber Defense by Unveiling AI-Powered Expert Systems and software engineering 2

Empowering Cyber Defense by Unveiling AI-Powered Expert Syst...

引用

2nd international conference on Foundation and Large Language Models, FLLM 2024

作者： Tawalbeh, Lo'Ai Franklin, John Ramirez, Christian Garcia, Jonathan Tawalbeh, Abdelrahman Muheidat, Fadi Jordan University of Science and Technology Department of Computer Engineering Irbid22110 Jordan Texas A&M University Department of Computing and Cyber Security San AntonioTX78224 United States Johnson High School San AntonioTX78259 United States School of Computer Science and Engineering California State University San BernardinoCA92407 United States

ISBN: (纸本)9798350354799

People all over the world are subject to cyber-attacks. The abundance of information on the internet gives hackers the tools to exploit vulnerabilities in computer systems, from home desktops to the most considerable servers hosted by organizations. Cyber security is a practice dedicated to defending electronic devices from cybercrime. Top cyber security practices apply expert systems that can detect patterns and adapt to changing variables in hacking methods. An expert system in computing is a complex program that utilizes artificial intelligence (AI) to simulate human tasks or calculate intricate functions. Artificial Intelligence techniques can be used to determine any abnormal activities and unusual data traffic in computer network which can be utilized to protect these networks against any malicious activities. Today, organizations seek the latest expert systems for cyber defense. This paper aims to describe the software engineering behind artificial intelligence and how it supports expert machines used in cyber defense systems. The intricate functions of artificial intelligence in cyber security are discussed, such as deep learning, intrusion detection trees, natural language processing, and artificial immune systems. © 2024 IEEE.

关键词： Cyber attacks

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Research on Key Technologies of Network Security Situational Awareness Based on Big Data 2

Research on Key Technologies of Network Security Situational...

引用

2nd IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2024

作者： Li, Yuxiang Guangdong University of Science and Technology Department of Computer Science Dongguan523083 China

ISBN: (纸本)9798350368208

This paper delves into the critical technologies for cybersecurity situational awareness in big data environments and proposes a comprehensive framework that integrates feature extraction, assessment and prediction. Firstly, in the feature extraction phase, autoencoders are employed to efficiently extract features from complex network data and identify potential security anomalies. Secondly, an improved Dempster-Shafer (D-S) evidence theory is used to assess cybersecurity threats. By introducing an expectation deviation function for uncertainty variables, the credibility of experts is optimized, and local conflicts among evidence are resolved. Finally, a hybrid model combining uncertain evidence theory with deep learning methods is constructed to predict potential attack scenarios. The model's predictive capabilities and computational efficiency are enhanced through optimization. The research results demonstrate that this framework significantly improves the accuracy and real-time performance of cybersecurity situation assessment, offering substantial social value in addressing cybersecurity challenges in big data environments. © 2024 IEEE.

关键词： Cybersecurity

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Evaluation of Semantic Failure Violations in Zookeeper 2

Evaluation of Semantic Failure Violations in Zookeeper

引用

2nd international conference on software engineering and Information Technology, ICoSEIT 2024

作者： Basith, Bagja Abdul Kusrini, Kusrini Utama, Hastari Universitas Amikom Yogyakarta Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Magister of Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350317503

This study aims to evaluate violations of semantic failures in Zookeepers. The main focus of the study was to identify and analyze situations where the zookeeper did not adhere to its semantic principles. Research methodology involves observing various scenarios and testing to understand the causes of semantic failures. The results of this study reveal concrete situations in which Zookeeper does not adhere to its semantic principles, thus providing deeper insight into the semantic failures of these systems. In research has been found the main source of semantic failure through observation of various scenarios and testing. This research is expected to lay the foundation for a better understanding of semantic failures in distribution coordination systems, particularly with regard to Zookeeper, as it provides a solid foundation for the development of more effective solutions to improve Zookeeper performance and reliability. when faced with a violation of semantic failure. © 2024 IEEE.

关键词： Semantics

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Self-Healing for software Defined Networking 2

Self-Healing for Software Defined Networking

引用

2nd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Biradar, Arun Chandan, Madhavarapu Raghavendra, Y.M. Chidambarathanu, K. Thamarai, I. Raturi, Anuj School of Computer Science and Engineering Reva University Karnataka Bengaluru560064 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Gsss Institute of Engineering and Technology for Women Department of Electronics and Communication Engineering Kamataka Mysuru570016 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Chennai City Campus Department of Computer Science and Engineering Tamil Nadu Chennai600029 India Graphic Era Deemed to Be University Department of Mechanical Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9781665456302

The advent of software-defined networking and virtualization of network functions has brought numerous advantages;however, to achieve the flexibility and programmability envisaged in these technologies, new components in the control and management planes were introduced. Such components require fast recovery because without management the entire data plane is inoperable. To deal with flaws in these plans, the self-healing technique is used, explored in the work that is summarized in this document. The results prove the self-healing efficiency in network slices with strict quality requirements and also demonstrate that the introduced framework is capable of self-healing, that is, healing the degraded environment as well as healing itself. © 2023 IEEE.

关键词： software defined networking

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Preliminary Study: Speech Emotion Recognition in Online Teaching From the Perspective of Educators Especially Late Deafened 2

Preliminary Study: Speech Emotion Recognition in Online Teac...

引用

2nd international conference on software engineering and Information Technology, ICoSEIT 2024

作者： Vyakaranam, Aparna Ramayah, Bavani Maul, Tomas University of Nottingham Malaysia Faculty of Science and Engineering Semenyih Malaysia

ISBN: (纸本)9798350317503

An effective Human computer Interaction (HCI) for recognizing human emotions automatically is known to as affective computing. Affective computing is achieved through programming, which is meant to detect emotions from facial expressions or verbal utterances. These detected emotions can be further used in many applications. Speech emotion recognition (SER) is one such affective computing application where the detected emotions can be used effectively, particularly in online education. Such an application would allow for the teaching strategies to be tailored for the students, on the basis of their understanding of the ongoing class, which is reflected in their verbal feedback. This valuable application of technology will enhance and support education for all, especially the educators who have issues with hearing verbal feedback from students. The main objective of this study is to find out from educators (with and without hearing impairment) on the necessity of knowing student emotions to enhance teaching outcomes for effective teaching and also the benefits of having an automatic detection system which would display student emotions in an online classroom setting. The outcome of this research clearly indicated how a preliminary user requirement study highlights the need and benefits of implementing a SER system. © 2024 IEEE.

关键词： Human computer interaction

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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2nd international conference on software engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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