2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献_第4页

Comparable GPU: Optimizing the BERT Model with AMX Feature 可比GPU:利用AMX特征优化BERT模型

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201262

Xiang Gao, Xiancheng Lin, Rongkai Liu

引用次数: 0

UCDN: A CenterNet-Based Dense Muti-scale Detection Fusion Net on Underwater Objects UCDN:基于centernet的水下目标密集多尺度检测融合网

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201320

Huipu Xu, Ying Yu, Xiangyang Long, Ziqi Zhu

{"title":"UCDN: A CenterNet-Based Dense Muti-scale Detection Fusion Net on Underwater Objects","authors":"Huipu Xu, Ying Yu, Xiangyang Long, Ziqi Zhu","doi":"10.1109/CCAI57533.2023.10201320","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201320","url":null,"abstract":"Underwater object detection is an inevitable task with urgent practical importance in the field of economic marine life. Due to the special underwater optical environment, most of the existing common detection algorithms are not capable of providing ideal results for underwater objects. For this reason, this paper proposes a constructive CenterNet-based underwater object detection model, named UCDN, accompanied by a detection strategy with pervasive applicability. Specifically, a detailed fusion module is created with the goal of filtering out interfering information. Meanwhile, we propose an exclusive idea of dense scale linking to fuse multi-scale features as much as possible. More importantly, we fuse our detection network with the Frankle-McCann Retinex algorithm to detect more objects obscured by the environment without increasing the training consumption. In addition to this, an efficient automatic sample balancing strategy is proposed, which is well suited to our detection situation. Finally, we evaluate our algorithm on underwater image datasets. The experiment results showed that the precision (mAP) of UCDN reached 87.46% which was higher than existing state-of-the-art land-based detection algorithms and underwater detection algorithms.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132522834","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

Self-Supervised Meta-Path-Based Heterogeneous Graph Embedding for Recommender Systems 基于自监督元路径的异构图嵌入推荐系统

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201255

Zeshun Zou, Youquan Wang

{"title":"Self-Supervised Meta-Path-Based Heterogeneous Graph Embedding for Recommender Systems","authors":"Zeshun Zou, Youquan Wang","doi":"10.1109/CCAI57533.2023.10201255","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201255","url":null,"abstract":"In recent years, significant progress has been made in research on recommender systems based on Heterogeneous Information Networks (HIN). However, most existing HIN-based recommender system methods rely on meta-path-based embedding models, which do not fully exploit the inherent homogeneous and contrastive difference information present in heterogeneous networks. In this study, we propose a method for learning representations by constructing graphs using both heterogeneous graph structures and homogeneous similarity graphs. We then apply contrastive loss to obtain embeddings that capture the differences between these two types of graphs. Our proposed recommendation method combines two perspectives through data embedding of the two types of graphs to train Graph Neural Networks (GNN). Specifically, we find that using primitive paths is the most effective way to directly embed heterogeneous graphs. Integrating and supplementing information rationally through realistic logic also makes sense. Additionally, supplementing data through similarity analogies between viewing sequences and users themselves is also meaningful. Through a twoview neighborhood selection process of logical relations and established facts, experiments show that our approach can improve HIN-based recommendation models.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1977 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131862","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

Survey of Neuromorphic Computing: A Data Science Perspective 神经形态计算概览:数据科学视角

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201289

Kasey Clark, Yalong Wu

引用次数: 0

Augmenting Radar Data via Sampling from Learned Latent Space 通过学习潜空间采样增强雷达数据

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201307

Daniel Scholz, Felix Kreutz, Pascal Gerhards, Jiaxin Huang, Florian Hauer, Klaus Knobloch, C. Mayr

引用次数: 0

Abnormal Detection of Industrial Control System Based on LSTM and GSK Algorithm Customized by Taguchi Method 基于LSTM和田口法自定义GSK算法的工控系统异常检测

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201287

Huiqi Zhao, Rui Lei, Fang Fan, Yulong Guo, Y. Li

{"title":"Abnormal Detection of Industrial Control System Based on LSTM and GSK Algorithm Customized by Taguchi Method","authors":"Huiqi Zhao, Rui Lei, Fang Fan, Yulong Guo, Y. Li","doi":"10.1109/CCAI57533.2023.10201287","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201287","url":null,"abstract":"Industrial control systems are at the core of critical national infrastructures such as petroleum, chemical, natural gas, power and metallurgy. However, with the integration of industrial control systems with the Internet, the Internet of Things and other network fields, industrial control systems have been penetrated by various security threats. At present, the existing anomaly detection methods have many limitations and cannot effectively identify various attacks. Therefore, in this paper, we propose an effective anomaly detection model for industrial control systems that combines Gaining-sharing knowledge based algorithm (GSK) and the LSTM network. Specifically, we first use the GSK algorithm for feature selection to eliminate redundant features, improve algorithm accuracy and reduce running time, and then use an LSTM classifier to classify different categories of attacks. Secondly, we used Taguchi method to customize the optimal solution for the GSK algorithm applied to the feature selection problem, which improves the efficiency and robustness of the algorithm. Furthermore, we experimentally validate the model using a real gas pipeline dataset. The experimental results show that the proposed TBGSK-LSTM model outperforms other traditional methods in terms of accuracy, precision, recall, F-score, average fitness function value and average number of selected features.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132860338","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

An Automated Level-Set Model Fusing Saliency Information for Sonar Image Segmentation 一种融合显著性信息的自动水平集模型用于声纳图像分割

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201312

Huipu Xu, Ziqi Zhu, Ying Yu, Xiangyang Long

{"title":"An Automated Level-Set Model Fusing Saliency Information for Sonar Image Segmentation","authors":"Huipu Xu, Ziqi Zhu, Ying Yu, Xiangyang Long","doi":"10.1109/CCAI57533.2023.10201312","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201312","url":null,"abstract":"Affected by the influences of various marine environments, sonar image is generally characterized by blurred target edges and uneven gray scale. Aiming at the difficulties of segmentation caused by such reasons, an automated level-set model integrating saliency information is proposed in this paper. This model includes two important parts: an automatic shadow removal algorithm based on pixels and a composite iterative segmentation strategy based on an improved level set method (LSM). First, shadows of the targets are extracted by color space transformation and replaced manually with pixels of the background area. Next, shadow removal is finished automatically by fusing saliency information from sonar image to reduce time complexity. Finally, a composite iterative strategy is proposed for sonar image with complex contents and blurred edges, where the initial contour of target is gradually optimized to the boundary of the target to achieve accurate segmentation. Qualitative and quantitative analysis experiments demonstrate that the proposed model has accurate target segmentation capability and is superior to other existing methods.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123536418","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

Repetition and Template Generalisability for Instance-Based Keystroke Biometric Systems 基于实例的击键生物识别系统的重复和模板通用性

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201300

Siôn Parkinson, Saad Khan, Na Liu, Qing Xu

{"title":"Repetition and Template Generalisability for Instance-Based Keystroke Biometric Systems","authors":"Siôn Parkinson, Saad Khan, Na Liu, Qing Xu","doi":"10.1109/CCAI57533.2023.10201300","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201300","url":null,"abstract":"Keystroke timings can be used as a behavioural biometric, enabling passive and non-intrusive authentication. Fixed-text keystroke biometric systems involve the acquisition of keypress timings when typing a single phrase. They can be used in conjunction with a standard password authentication system to provide an increased level of security. Design decisions need to be made regarding the different technical aspects (e.g., feature sets, matching mechanism, etc.) of the system and there is a wealth of literature to guide this process. However, there is an absence of knowledge available when it comes to understanding how repetitions in user samples and characteristics of the password provided over an extended timeline can impact the system’s accuracy. In this paper, timings are collected from 65 participants, who are required to type the same passwords 4 times per week for 8 weeks, yielding a total of 81,920 timing datasets. A systematic analysis is then performed for each of the 8 weeks, following the same template creation and matching process, to gain an understanding of which week’s timings produce more generalised templates, providing a lower Equal Error Rate when matched against samples from all weeks.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121862187","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

Target Detection Based on Taylor Expansion and Bilateral Symmetric Network 基于Taylor展开和双边对称网络的目标检测

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201291

Zhihui Li, Xiaoshuo Jia, Shuhua Li, Suping Liu

{"title":"Target Detection Based on Taylor Expansion and Bilateral Symmetric Network","authors":"Zhihui Li, Xiaoshuo Jia, Shuhua Li, Suping Liu","doi":"10.1109/CCAI57533.2023.10201291","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201291","url":null,"abstract":"In the task of target detection, traditional detection algorithms are prone to weak generalization and low detection accuracy due to the small number of parameters and fixed parameter values. Conversely, CNN-based detection algorithms are more accurate, but cannot be developed on mobile due to model issues. In this paper, we refer to the gradient features of HOG and propose the Taly preprocessing method which can preprocess images using Taylor extensions and extract multi-order gradient features of the images. Then TaylorNet is designed under the bilateral symmetric network. Multi - gradient features contain rich edge feature information of images. Then, the gradient feature is fused through the bilateral symmetrical network structure to achieve the fusion of low resolution and high resolution, so as to realize the accurate positioning and detection of edge features, and finally achieve the accurate detection of the target. Through the training and testing of the dataset SBD and VOC2012, the comparison results show that compared with some SOTA algorithms, TaylorNet effectively reduces the size of the model while ensuring high accuracy, so it can also effectively implement mobile development.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128774742","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

LD-ABR: An Adaptive Bitrate Algorithm for Video Transmission in Wireless Network LD-ABR:一种无线网络视频传输的自适应比特率算法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201241

Chunlei Chen, Kaijun Liu, Chen Dong, Geng Liu

{"title":"LD-ABR: An Adaptive Bitrate Algorithm for Video Transmission in Wireless Network","authors":"Chunlei Chen, Kaijun Liu, Chen Dong, Geng Liu","doi":"10.1109/CCAI57533.2023.10201241","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201241","url":null,"abstract":"The popularity of mobile terminals has made video transmission in wireless scenarios important. However, achieving high viewing quality video transmission between base and mobile terminals is a difficult problem. The unpredictable fading and noise generated in wireless scenarios cause drastic fluctuations, making existing adaptive bit rate algorithms unable to adapt to the rapidly changing volatility and long tail problems in the network. In this paper, we introduce LSTM-D3QN Adaptive Bitrate Algorithm (LD-ABR), a new reinforcement learning Adaptive Bitrate Algorithm. LD-ABR uses long and short-term memory (LSTM) to predict throughput, and uses video bit rate, bit rate switching frequency, network speed and video pause time for bit rate selection to better cope with the complex changes in wireless networks. Finally, LD-ABR is compared with Comyco and Pensieve algorithms and the results show that LD-ABR has better performance in wireless network environment. Under the worst network conditions, Pensieve mode has a 16% chance of stopping, Comyco has a 7% chance, and LD-ABR mode has only a 1% chance, and its QoE index is more than 30% higher than Pensieve.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127627172","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