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2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献

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Research on New Teaching and Management Mode of Colleges and Universities Based on Intelligent Technology 基于智能技术的高校教学管理新模式研究
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807719
Chenggong Zhai, Jianxiang Li, Huifang Lv, Xing Zhang, Rangmin Wu, Pengcheng Zhou
{"title":"Research on New Teaching and Management Mode of Colleges and Universities Based on Intelligent Technology","authors":"Chenggong Zhai, Jianxiang Li, Huifang Lv, Xing Zhang, Rangmin Wu, Pengcheng Zhou","doi":"10.1109/CCAI55564.2022.9807719","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807719","url":null,"abstract":"The construction of a new teaching and management mode in Colleges and Universities Based on intelligent technology is studied by combining online and offline teaching methods This paper expounds the current development of intelligent technology, as well as the current situation and existing problems of teaching management in Colleges and universities, and puts forward that by establishing intelligent interconnected information network, building intelligent service software platform, building an intelligent environment with virtual and real integration, and building a new teaching management model with intelligent integration, we can not only accumulate experience for the overall upgrading of information construction in Colleges and universities, It is more beneficial to promoting high-quality education.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122155045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Privacy-preserving Hybrid Cryptosystem Based on Chaos Theory and ECC Algorithm 基于混沌理论和ECC算法的隐私保护混合密码系统
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807794
Yu Liu, Haopeng Tong, Nong Si
{"title":"Privacy-preserving Hybrid Cryptosystem Based on Chaos Theory and ECC Algorithm","authors":"Yu Liu, Haopeng Tong, Nong Si","doi":"10.1109/CCAI55564.2022.9807794","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807794","url":null,"abstract":"Data encryption is a practical approach to protect biomedical image information security. Towards the massive amount of data generated in the medical field, chaotic model cryptosystems, with high sensitivity to initial conditions and overall stability and randomness of the system, are inevitably becoming an appropriate platform for communication protection. This paper proposes a privacy-preserving Hybrid encryption scheme based on Chaos theory and ECC Algorithm (HCEA) for the secure delivery of medical data. Specifically, we employ the logistic map with randomness characteristics to protect published data privacy against the complex network environment and other non-subscribers. The proposed cryptosystem can essentially achieve the secrecy effect of a “one-time pad.” To achieve double encryption of textual information and keys, we propose an improved image steganography technique with secondary scrambling encryption of carrier images by the MLNCML system, enhancing encryption efficiency and security. Different from existing standard LSB image encryption methods, the HCEA can encrypt initial parameters of logistic and MLNCML with the asymmetric encryption ECC algorithm while guaranteeing the security of key distribution in complex network environments. The security proof and performance evaluation show that the proposed HCEA scheme is secure in medical data transmission and effective in practice.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Short Text Topic Model Based on Semantics and Word Expansion 基于语义和词扩展的短文本主题模型
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807822
Li Zhen, Shao Yabin, Yang Ning
{"title":"A Short Text Topic Model Based on Semantics and Word Expansion","authors":"Li Zhen, Shao Yabin, Yang Ning","doi":"10.1109/CCAI55564.2022.9807822","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807822","url":null,"abstract":"In recent years, with the increasing amount of short text information, there are more and more researches on short text information, and the topic information analysis of short texts is one of the key researches. In order to overcome the sparsity problem of short text datasets, this paper conducts research on the basis of the short text topic model Biterm Topic Model (BTM). Aiming at the problem of lack of semantic association in BTM model, this paper proposes a biterm acquisition method based on semantic dependencies. The method firstly apply semantic analysis on the text, and then combines words with strong correlation into biterm. The semantic relevance between words in biterm is enhanced. In order to further solve the text sparse problem, this paper proposes to expand the number of biterms based on similarity calculation of words and calculation of relationship between words. This method not only solves the sparsity problem, but also enhances the topic tendency of text.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124765752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
LAN Network Optimization after a DDoS Attack Detected with Supervised Learning 监督学习检测DDoS攻击后的局域网网络优化
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807697
Diego Vallejo-Huanga, Santiago Vizcaíno
{"title":"LAN Network Optimization after a DDoS Attack Detected with Supervised Learning","authors":"Diego Vallejo-Huanga, Santiago Vizcaíno","doi":"10.1109/CCAI55564.2022.9807697","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807697","url":null,"abstract":"The Distributed Denial of Service (DDoS) attack is one of the most dangerous cyberattacks on the Internet, so can affect any server on any type of network, causing connectivity problems and even total loss of services. Machine learning can solve computational security problems and is frequently used to defend against cyber attacks. This article proposes the construction of a network topology where several DDoS attacks were applied, which will be detected by three Machine Learning classification algorithms. A dataset was generated from the collection of packets circulating in the network with samples of normal traffic and malicious packets, on which the experimental tests were carried out. In the classification task, the best performing supervised learning algorithm was Random Forest, with an accuracy of 100%. Finally, upon detecting a DDoS attack on the network, Dijkstra’s optimization algorithm is applied to find an alternative route to mitigate network oversaturation. Two scenarios were proposed, the first analyzes the optimal route in an attacked network and the second without attacks. The results show a reconfiguration in the network to avoid routes where DDoS attack detection was applied.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132326838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MiTU-Net: An Efficient Mix Transformer U-like Network for Forward-looking Sonar Image Segmentation MiTU-Net:一种用于前视声纳图像分割的高效混合变压器u型网络
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807763
Yingshuo Liang, Xingyu Zhu, Jianlei Zhang
{"title":"MiTU-Net: An Efficient Mix Transformer U-like Network for Forward-looking Sonar Image Segmentation","authors":"Yingshuo Liang, Xingyu Zhu, Jianlei Zhang","doi":"10.1109/CCAI55564.2022.9807763","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807763","url":null,"abstract":"The segmentation of forward-looking sonar (FLS) image could assist underwater vehicles to recognize and measure underwater crash objects. Due to the complex noise and blurred object edge information in FLS image, the accurate segmentation result requires the model to have strong feature extraction ability. The CNN-based semantic segmentation networks focus too much on local information, which may amplify the complex noise. And their computational overhead is high. To address these problems, we construct a novel efficient Mix Transformer U-like network named MiTU-Net for FLS image segmentation. In addition, we introduce the online hard example mining (OHEM) crossentropy loss function to improve the learning ability of hard samples in dataset. We have carried out a series of experiments on the self-made FLS dataset. The experimental results demonstrate that MiTU-Net has better performance than other methods, and it shows effectiveness and robustness for FLS image segmentation task.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124152533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Deep Q-network Based Reinforcement Learning for Distributed Dynamic Spectrum Access 基于深度q网络的分布式动态频谱接入强化学习
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807797
Manish Anand Yadav, Yuhui Li, Guangjin Fang, Bin Shen
{"title":"Deep Q-network Based Reinforcement Learning for Distributed Dynamic Spectrum Access","authors":"Manish Anand Yadav, Yuhui Li, Guangjin Fang, Bin Shen","doi":"10.1109/CCAI55564.2022.9807797","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807797","url":null,"abstract":"To solve the problem of spectrum scarcity and spectrum under-utilization in wireless networks, we propose a double deep Q-network based reinforcement learning algorithm for distributed dynamic spectrum access. Channels in the network are either busy or idle based on the two-state Markov chain. At the start of each time slot, every secondary user (SU) performs spectrum sensing on each channel and accesses one based on the sensing result as well as the output of the Q-network of our algorithm. Over time, the Deep Reinforcement Learning (DRL) algorithm learns the spectrum environment and becomes good at modeling the behavior pattern of the primary users (PUs). Through simulation, we show that our proposed algorithm is simple to train, yet effective in reducing interference to primary as well as secondary users and achieving higher successful transmission.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114919751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Improved Object Detection Method for Unmanned Surface Vehicle Using Real-Time Neural Networks 基于实时神经网络的改进无人水面车辆目标检测方法
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807800
Hong Wang, W. Zhang, Y. Wen, Shanxing Qin
{"title":"Improved Object Detection Method for Unmanned Surface Vehicle Using Real-Time Neural Networks","authors":"Hong Wang, W. Zhang, Y. Wen, Shanxing Qin","doi":"10.1109/CCAI55564.2022.9807800","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807800","url":null,"abstract":"Real-time and accurate object detection is a critical (b) prerequisite for Unmanned Surface Vehicles(USVs) to perform intelligent tasks based on images or videos. While the maritime environment always encountered various extreme scenarios, such as rainy or foggy weather, strong lights and far vision, which all seriously harmed the performance of state-of-the-art methods for normal object detection when directly applied them on USV. Therefore, we proposed an improved object detection method for USV based on Yolov4, which focus on repairing the performance loss caused by the unfavorable factors under maritime environment. Firstly, we adjust the default anchor size in ordinary model which helps detect the tiny object from a far vision, as well as the anchor ratio, fitting the shape of ships more to implicitly improve the detection precision. Secondly, we take full advantage of data augmentation to increase the robustness of object detection under extreme brightness. Finally, we enriched our training data with more rainy and foggy images token from different maritime scenes which enhanced model’s ability to detect objects under extreme weather. Extensive experiments demonstrates that proposed improved method effectively achieved real-time and accurate object detection for USV under maritime environment.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115099439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Multi-UAV Data Collection Optimization for Sink Node and Trajectory Planning in WSN 基于汇聚节点的多无人机数据采集优化与WSN轨迹规划
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807699
Mesfin Leranso Betalo, S. Leng, Longyu Zhou, Maged Fakirah
{"title":"Multi-UAV Data Collection Optimization for Sink Node and Trajectory Planning in WSN","authors":"Mesfin Leranso Betalo, S. Leng, Longyu Zhou, Maged Fakirah","doi":"10.1109/CCAI55564.2022.9807699","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807699","url":null,"abstract":"Unmanned Areal Vehicles (UAVs) can be employed for temporary missions with flight ability while exposing limited flight energy and time due to restricted battery life. In this paper, to minimize the total energy consumption of both UAVs, the effective use of sink nodes’ power, we optimize both the number of sink nodes and the trajectories of multiple UAVs in WSN. In our scenario, all UAVs start their mission from the location of the charging station and back into the same charging station after finishing their data collection tasks. Specifically, we select the number of sink nodes using the Genetic Algorithm to maximize the lifetime WSN. The Multiple Traveling Salesman Problem (MTSP) based path planning algorithm is proposed to solve the trajectory using Held-Karp lower bound method for the trajectory path of UAV. The particle swarm optimization (PSO) and GA algorithm are demonstrated to get the feasible performance solution of the simulation results.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114776663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CCAI 2022 Cover Page CCAI 2022封面
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/ccai55564.2022.9807769
{"title":"CCAI 2022 Cover Page","authors":"","doi":"10.1109/ccai55564.2022.9807769","DOIUrl":"https://doi.org/10.1109/ccai55564.2022.9807769","url":null,"abstract":"","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133445385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Service Selection Based on Bat Algorithm in Hybrid Cloud-Edge Computing 混合云边缘计算中基于Bat算法的服务选择
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807801
Yunxuan Wang, Chen Liu
{"title":"Service Selection Based on Bat Algorithm in Hybrid Cloud-Edge Computing","authors":"Yunxuan Wang, Chen Liu","doi":"10.1109/CCAI55564.2022.9807801","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807801","url":null,"abstract":"Handing over edge network data and computation to cloud platforms often results in high bandwidth costs and processing delays, which makes hybrid cloud-edge computing a hot topic for research and application in recent years. If reasonable service selection can be performed on mobile edge gateways, not only can this problem be well solved, but also the energy cost of mobile devices can be reduced. In this paper, we first design a time and energy cost model that considers the latency and energy cost of three components: edge devices, cloud servers, and data transmission, and convert the service selection of minimizing the overall latency under the completion of energy constraints into a nonlinear programming problem. Then we design a bat algorithm to solve the above problem. Furthermore, we design an adaptive chaos bat algorithm to optimize the solution space so that it avoids falling into local optimal solutions. Eventually, simulation results show that the proposed algorithm is superior to other algorithms in terms of overall time delay optimization and has a better stability.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114504085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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