Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence最新文献_第10页

Predict Inter-photo Visual Similarity via Pre-trained Computer Vision Models 通过预训练的计算机视觉模型预测照片间的视觉相似性

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579769

H. Omori, K. Hanyu

{"title":"Predict Inter-photo Visual Similarity via Pre-trained Computer Vision Models","authors":"H. Omori, K. Hanyu","doi":"10.1145/3579654.3579769","DOIUrl":"https://doi.org/10.1145/3579654.3579769","url":null,"abstract":"There were three types of photo sets, 200 student life photos, 242 townscapes, and 100 garden landscapes. The student life photos included various photos, including landscapes, objects, people, and food. On the other hand, there were only garden photos in the garden landscapes. There was some variation of photos in the townscapes. The inter-photo visual similarity had been measured manually for each photo set. Dividing the photo set randomly into training and test data parts, the visual similarity was predicted via pre-trained computer vision models (CVMs), such as CNN, Vision Transformer (ViT), and CLIP. The prediction accuracy could be measured by the trace of the correlation matrix between the original and restored three dimensional MDS coordinates aligned by the Procrustes transformation. Three photo sets were used as a benchmark for the predictive power of CVMs. The image features by ViT models pre-trained on ImageNet-21K and by image encoders of CLIP showed high predictive power in any photo set. Combining different CVMs increased the predictive power. The MDS first axis was well restored for any photo set. For the student life photos with a large photo variation, the MDS from the visual similarity and the MDS from the best CVM was found almost the same, so there seemed no need to predict the visual similarity. For the garden landscapes with a small photo variation, the visual similarity prediction using CVMs was not so successful in the second and third axis of MDS.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131997530","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

Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis 基于模糊线性判别分析的多视图图学习

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579708

Hong Jia, Jian Huang

引用次数: 0

Self-attention mechanism-based SAR for YOLO-v3 maritime ships image target detection 基于自关注机制的YOLO-v3型舰船SAR图像目标检测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579668

Xinyu Li, Zhongxun Wang, Mengyu Zhang

引用次数: 0

Multi-label co-occurrence network for stealing electricity type detection and research on forensics sequence rules 多标签共现网络窃电型检测及取证顺序规则研究

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579763

Runan Song, Yang Xue, P. Zhang, Yining Yang, Cong Wang

{"title":"Multi-label co-occurrence network for stealing electricity type detection and research on forensics sequence rules","authors":"Runan Song, Yang Xue, P. Zhang, Yining Yang, Cong Wang","doi":"10.1145/3579654.3579763","DOIUrl":"https://doi.org/10.1145/3579654.3579763","url":null,"abstract":"With the development of economy and the progress of technology, the anti-stealing and investigation work in power consumption management has become extremely complex and arduous. In the complex environment of the electricity stealing scene, due to the hidden electricity stealing and the complex on-site power distribution relations, it is difficult to carry out the on-site evidence collection work smoothly. This paper focuses on the stealing electricity type detection and the process of on-site evidence collection, deeply analyzes the key elements of electricity stealing, and constructs a stealing electricity type detection model based on multi label graph convolution network. It uses GRU network to mine the time sequence characteristics of power consumption, learns the co-occurrence relationship of electricity stealing labels based on GCN network, and combines the user attribute characteristics to ultimately improve the accuracy of stealing electricity type detection. Based on the above, by analyzing the key elements such as the historically collected data, we use the method of combining expert experience and statistical analysis to generate the forensics sequence rules of evidence collection for different types of electricity stealing, thus effectively improving the efficiency and quality of evidence collection for on-site staff. The experimental results demonstrate that the proposed methods perform favorably against the most frequently used methods.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126918688","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

High-performance ultrasonic beamforming algorithm based on deep learning 基于深度学习的高性能超声波束形成算法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579678

Qiong Zhang, Yong-Jian Kuang, Zhengnan Yin

引用次数: 0

Dense Points Aggregation for Efficient and Collaborative Earth-Imaging Task Planning 基于密集点聚合的高效协同地球成像任务规划

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579705

Youmei Pan, Peng Wang, X. Hui, Jinwen Li

{"title":"Dense Points Aggregation for Efficient and Collaborative Earth-Imaging Task Planning","authors":"Youmei Pan, Peng Wang, X. Hui, Jinwen Li","doi":"10.1145/3579654.3579705","DOIUrl":"https://doi.org/10.1145/3579654.3579705","url":null,"abstract":"The continuous development of high-resolution imagery satellite payloads is featured with lower cost, high-integration and smaller volume, which promotes the wide adoption of Earth-imaging facilities on satellite in order to improve sensing coverage, quality, efficiency and so on. Satellite task planning is crucial in automatically generating observation timelines to fulfill the Earth-imaging tasks by optimizing the usage of satellite resources. As an important category of ground sensing tasks, points of interest(PoIs) are very common for satellite to sense ground changes such as building collapse in earthquake, social hot spots, volcano eruption, etc. As the number of PoIs increases enormously, separately planning for each PoI sensing task is no more realistic and aggregating the dense ground points for collaboratively imaging them through multi-satellite can provide a new solution for constellation applications. In this paper, a novel ground PoIs aggregation method is proposed to decrease task manipulation frequencies of satellites, based on which a collaborative multi-satellite planning is modeled and solved by particle swarm optimization. The efficacy of the aggregation-collaboration manner is evaluated and demonstrated in the experiment.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124099619","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 Efficient Alternative to Subgraph Isomorphism and Its Advantages 子图同构的一种有效替代方法及其优点

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579768

W. Zhang, George P. Chan, Wai Kin Victor Chan

{"title":"An Efficient Alternative to Subgraph Isomorphism and Its Advantages","authors":"W. Zhang, George P. Chan, Wai Kin Victor Chan","doi":"10.1145/3579654.3579768","DOIUrl":"https://doi.org/10.1145/3579654.3579768","url":null,"abstract":"Subgraph Isomorphism is a fundamental problem in graph theory. It has many applications in social network analysis, molecular investigations, knowledge graphs, etc. Given a Query Graph and a Data Graph, the target of Subgraph Isomorphism, i.e., Subgraph Matching, is to determine if this Query Graph is isomorphic to any subgraph of the Data Graph. This work proposes a new type of Query Graph, combined with multiple general Query Graphs. We call it Compulsory-Optional Query Graph (CO Query Graph). This new type of Query Graph contains all the vertices in the combined general Query Graph, and each vertex corresponds to a search priority. Based on CO Query Graph, the previous multiple match processes can be reduced to one. It tremendously improves search efficiency. The Subgraph Isomorphism based on this new kind of Query Graph is an extension and improvement of the previous Subgraph Isomorphism studies. We propose a backtracking-pruning-based CO solver (BPC). This algorithm builds on the backtracking-pruning framework. BPC modifies the output criterion and matching conditions to satisfy the CO query context. A case study of real-world graph data illustrates that BPC built on CO Query Graph is more efficient than conventional Query Graphs. To verify the effectiveness of our method, we conducted experiments on the synthetic graph and real-world data. The results show that the BPC can significantly reduce the search space and improve the search efficiency in the recursive calls and the response time. Experiments resulting from synthetic graph data analysis allow us to primarily identify the critical factor that affects the efficiency of the BPC primarily.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126958255","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

Multi-Objective Optimization of Dynamic Resource Scheduling in IoT Cloud Platform 物联网云平台中动态资源调度的多目标优化

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579731.3579805

Ran Li, Hailong Zhang, Enguo Zhu, Yi Ren

{"title":"Multi-Objective Optimization of Dynamic Resource Scheduling in IoT Cloud Platform","authors":"Ran Li, Hailong Zhang, Enguo Zhu, Yi Ren","doi":"10.1145/3579731.3579805","DOIUrl":"https://doi.org/10.1145/3579731.3579805","url":null,"abstract":"In the Internet-of-Things (IoT) cloud platform, optimizing resource scheduling is the main way to achieve the maximum benefit of the system. However, the current researches lack an effective solutions to manage the steady and the abnormal state changes of batch tasks as a whole. To solve the problem of cloud resource scheduling for batch tasks under different scenarios and achieve the maximum benefit of the power IoT cloud platform, this paper proposes a Multi-Objective Optimization Model (MOOM) for dynamic resource scheduling. Firstly, we analyze the task execution performance parameters under the steady state, and proposes a performance analysis model based on queuing theory. Based on the analysis model, we can calculate the approximate solution of task performance parameters under a certain configuration. Then, considering different operation scenarios of the power IoT, a dynamic scheduling mechanism for cloud resources is constructed based on the performance parameters, which can guide the cloud platform to determine the optimal resource scheduling scheme under a given scenario. In addition, MOOM also contains the optimization objective of cost minimization, and proposes a method to quantify the cost. Finally, extensive experimental evaluations demonstrate the efficiency and effectiveness of our proposed model.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125468674","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

Top-k node identification method based on Gaussian plume model 基于高斯羽流模型的Top-k节点识别方法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579748

Xu Cao, F. Yin

引用次数: 0

Life assessment method of electronic components based on reliability factor sharing model 基于可靠性因子共享模型的电子元件寿命评估方法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579669

Linlin Shi, Peiliang Yang, Bin Zhou, Si Chen, Zhenwei Zhou, Danni Hong

引用次数: 0