Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence最新文献_第2页

Deep reinforcement learning based delay-aware task offloading for UAV-assisted edge computing 基于深度强化学习的无人机辅助边缘计算延迟感知任务卸载

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579736

Mingxuan Huang, Kaixuan Sun, Yunpeng Hou, Zhicheng Ye, Yuanlong Wan, Huasen He

{"title":"Deep reinforcement learning based delay-aware task offloading for UAV-assisted edge computing","authors":"Mingxuan Huang, Kaixuan Sun, Yunpeng Hou, Zhicheng Ye, Yuanlong Wan, Huasen He","doi":"10.1145/3579654.3579736","DOIUrl":"https://doi.org/10.1145/3579654.3579736","url":null,"abstract":"Multi-access edge computing has been widely used in various Internet of Things (IoT) devices because of its excellent computing and fast interaction abilities. How to improve the service extensibility of edge computing and optimize the computing offload strategy has become the key to improve the quality of service to edge computing users. However, the traditional offloading strategy based on mathematical programming has exposed its inherent limitations in dynamic scenarios, and cannot meet the requirements of multiple-mobile terminals distributed in a large area. Therefore, this paper use Unmanned Aerial Vehicles (UAVs) to establish a multi-UAV-assisted edge computing framework for extending the service range, and proposes an offloading strategy based on reinforcement learning to offload the growing computing requirements from mobile terminals to edge servers. By mapping the states of mobile terminals and UAVs to the corresponding action space, and then offloading computing tasks to UAVs, the energy consumption caused by computing and processing tasks of mobile terminals can be effectively reduced. Jointly considering the potential dimensional disaster of state space and the convergence failure imposed by the increase of device numbers, a novel computation offloading strategy based on deep reinforcement learning is proposed. Moreover, we design a load-balancing mechanism in the UAVs to improve the processing capacity. Experimental results prove that our proposed algorithm can effectively reduce the computing energy consumption of mobile terminals and avoid task timeout with a short convergence time.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"36 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":"122259676","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

Physical design of fusion based on generative adversarial networks 基于生成对抗网络的融合物理设计

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579760

W. Xiong, Pan Liu, Zhangchun Tang, Yan Shi, Chencheng Liu, Fanyu Qu, Gaoyang Liu, Qiang Gao

引用次数: 1

Wave-iPCRNet: Toward point cloud registration in electronics manufacturing by a quantum-inspired iterative point cloud registration network Wave-iPCRNet:量子启发迭代点云配准网络在电子制造中的点云配准

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579703

Quan Zhong, Huafeng Dai, Jun Shao, Jyun-Rong Wang, Tao Chen, Hao Liu

{"title":"Wave-iPCRNet: Toward point cloud registration in electronics manufacturing by a quantum-inspired iterative point cloud registration network","authors":"Quan Zhong, Huafeng Dai, Jun Shao, Jyun-Rong Wang, Tao Chen, Hao Liu","doi":"10.1145/3579654.3579703","DOIUrl":"https://doi.org/10.1145/3579654.3579703","url":null,"abstract":"Point cloud registration is one of the important 3D vision tasks. It is a fundamental task of the downstream tasks such as 3D tracking, autonomous driving, 3D reconstruction, and pose estimation. The iterative point cloud registration network (iPCRNet) model is developed by the team of Carnegie Mellon University et al., using point cloud data directly to perform the point cloud registration task. On the other hand, the defect detection in the electronic manufacturing has the requirements of minimal inference time and training cost. Despite the transformer module has achieved good performance on this task, its computation cost increase rapidly while the input data points increased than other modules. Hence, considering these requirements the multi-layer perceptron (MLP) module is usually used. However, the simple MLP module performance on the network design may have some improvements can be done. This work proposed a Wave-iPCRNet model using the quantum-inspired Wave-MLP module achieving the state-of-the-art performance on numerous 2D tasks. On the ModelNet40 benchmark dataset, the Wave-iPCRNet improves the test loss of iPCRNet from 0.038 to 0.031, improving the rotation error of iPCRNet from 15.153 to 14.104, and improving the translation error of iPCRNet from 0.007 to 0.006.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"25 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":"116570941","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

Research on the medication regularity of traditional Chinese medicine for common chronic diseases based on association rules 基于关联规则的常见慢性病中药用药规律研究

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579664

Renmin Wang, Jie Li, Yuanyuan Wang

{"title":"Research on the medication regularity of traditional Chinese medicine for common chronic diseases based on association rules","authors":"Renmin Wang, Jie Li, Yuanyuan Wang","doi":"10.1145/3579654.3579664","DOIUrl":"https://doi.org/10.1145/3579654.3579664","url":null,"abstract":"Chronic diseases are the kind of diseases that cause the most severe disease burden in China and have brought significant challenges to the health of our people. With the increase of its global prevalence, it has become a serious global public health problem. Association rules can be used to mine the high-frequency groups of traditional Chinese medicine treating common chronic diseases and the strong association between them and find valuable information hidden in medical data sets. This study uses the FP-growth algorithm to mine and analyzes the Chinese patent medicine prescriptions for five common chronic diseases. The primary purpose is to use association rule mining technology to mine the hidden patterns in traditional Chinese medicine prescriptions for treating chronic diseases and to provide chronic disease medical personnel and related researchers with the characteristics and laws of traditional Chinese medicine for treating chronic diseases, which has significant theoretical value for further understanding and innovating traditional Chinese medicine treatment methods for chronic diseases.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"185 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":"122771223","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

Exploiting Semantic Space to Enhance Event Detection Combined with Event Knowledge 结合事件知识利用语义空间增强事件检测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579733

Jinshang Luo, Mengshu Hou

引用次数: 0

A Method for power system state assessment and analysis based on PSO algorithm 基于粒子群算法的电力系统状态评估与分析方法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579744

Wei Zhou, Xinlei Cai, Jinzhou Zhu, Xu Lin, Xinglang Xie

引用次数: 0

Object detection of linear structures on Mars based on YOLO network 基于YOLO网络的火星线性结构目标检测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579750

Jingkun Xu, Jiarui Liang, Pengcheng Yan, Xiaolin Tian

{"title":"Object detection of linear structures on Mars based on YOLO network","authors":"Jingkun Xu, Jiarui Liang, Pengcheng Yan, Xiaolin Tian","doi":"10.1145/3579654.3579750","DOIUrl":"https://doi.org/10.1145/3579654.3579750","url":null,"abstract":"The geographic feature of terrestrial planets is a critical and important reference that could help researchers have a further understanding of planetary history and its evo-lution. Traditionally, the detection of specific landforms and their geographic parameter extraction basically relies on manual marking, these types of approach may consume a lot of labor and time costs. On the other side, with the development of convolutional neural networks (CNNs),it is able to handle more complicated tasks such as object detection and semantic segmentation with high efficiency and accuracy. In order to solve this problem, this paper presents an introduction about using neural network to do object detection of the linear structures on Mars, like dorsum, fossa and so on. Based on the DEM data of Mars, this paper makes a linear structure data set. The neural network be used is YOLO-v5. In the test of 300 iterations, the algorithm can get the best detection results when it iterates 200 times, the accuracy of the object detection can reach 81% when mAP = 0.5. The results show that the method proposed in this paper can effectively judge whether there is a linear structure in the graph and mark it, which can be used to assist scientists to reduce the time cost required for detection.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"60 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":"133739969","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

A Cloud-Assisted Data Processing Scheme for Smart Grid with Flexible Access Control 一种具有灵活访问控制的智能电网云辅助数据处理方案

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579693

Shi Chen, JingJing Wang, Zhengxuan Yu, Huan Xu, Chenxi Dong

{"title":"A Cloud-Assisted Data Processing Scheme for Smart Grid with Flexible Access Control","authors":"Shi Chen, JingJing Wang, Zhengxuan Yu, Huan Xu, Chenxi Dong","doi":"10.1145/3579654.3579693","DOIUrl":"https://doi.org/10.1145/3579654.3579693","url":null,"abstract":"Compared with traditional power grid, smart grid provides more efficient power transmission and real-time data acquisition. However, data processing in smart grid involves heavy overheads in storage and computation, and user privacy needs to be considered. In order to address these issues, many privacy preserving data aggregation schemes have been proposed for smart grid, and it has been suggested to outsource the computation and storage to the cloud platform for performance improvement. However, most of the existing solutions still suffer some limitations. First, a single cloud server is normally used to process the data, and sensitive information might be leaked if this server is malicious. Second, proper access control is crucial for data protection, but many schemes have not considered this feature. Therefore, the existing solutions still cannot meet the requirements in practical applications. In this paper, we propose a cloud-assisted secure data processing scheme for smart grid with flexible access control. Smart meters use encryption to protect the data, while the decryption is performed in a distributed fashion. Moreover, attribute based encryption (ABE) is applied to realise flexible access control for the power consumption data. Therefore, our scheme achieves both privacy protection and access control. Since most of the expensive operations can be outsourced to the cloud platform, our scheme is very efficient for practical use.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"48 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":"124745503","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

Quality-aware aggregated conformal prediction for silent speech recognition 无声语音识别的质量感知聚合共形预测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579762

Kai Xu, Wei Zhang, Ming Zhang, You Wang, Guang‐hua Li

{"title":"Quality-aware aggregated conformal prediction for silent speech recognition","authors":"Kai Xu, Wei Zhang, Ming Zhang, You Wang, Guang‐hua Li","doi":"10.1145/3579654.3579762","DOIUrl":"https://doi.org/10.1145/3579654.3579762","url":null,"abstract":"As a kind of terminal neural signal, electromyography (EMG) generated by articulatory muscles is widely used in silent speech recognition (SSR). The SSR task can be converted to a pattern recognition problem, where silent speech patterns correspond to clusters in the feature space. Conventional inductive conformal predictor (ICP) based solutions in the SSR face limitations in the subsampling. In addition, the data quality assessment is a long-standing challenge on the recognition ability of models, but it is seldom discussed in previous works. We introduce the aggregated conformal prediction (ACP) to replace ICP, which enhances the diversity of subsampling to solve the problem. With the purpose of digging out the high-quality extended dataset in data expansion, this study proposes the Supervised K-means Evaluation (SKE) method. Equipped with SKE method, ACP framework contributes to an efficient and robust SSR solution, and the advantages have been validated on the task for Chinese words. Our results support it is significant to design SKE method and utilize ACP framework. To our knowledge, this study is the first to apply ACP methodology and quantitatively evaluate the data quality in the SSR task.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"355 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":"132759657","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

Combining Recurrent and Convolution structures: A Deep Compressed Sensing Network for Sparse Signal Recovery 结合循环和卷积结构:用于稀疏信号恢复的深度压缩感知网络

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579718

Zhongjun Liu, Jun Zhang, Luhua Wang

{"title":"Combining Recurrent and Convolution structures: A Deep Compressed Sensing Network for Sparse Signal Recovery","authors":"Zhongjun Liu, Jun Zhang, Luhua Wang","doi":"10.1145/3579654.3579718","DOIUrl":"https://doi.org/10.1145/3579654.3579718","url":null,"abstract":"With the powerful learning ability of neural networks, compressive sensing methods based on deep neural networks have shown great potential. However, most existing methods treat the neural network as a black box, which weakens the regularization of \"known\" signal priors. In order to solve this problem, a class of compressed sensing methods based on deep unfolding has been proposed in recent years. By mapping the iterative optimization algorithm in the classical model-based methods onto networks, the method based on deep unfolding integrates the advantages of both, making the network interpretable while greatly reducing the time complexity. In this paper, to solve the problem of sequential sparse reconstruction, we propose a novel model based on deep unfolding, dubbed DW-FISTA. The whole model can be divided into two modules, namely the mapping module and the refine module. The first module maps the iterative process of the fast iterative shrinkage-thresholding algorithm (FISTA) to an interpretable recurrent neural network composed of fixed phases. Compared with other ISTA models based on deep unfolding, our model has a better global convergence rate. In the second module, we use convolution filters and nonlinear activation functions to refine the reconstructed signal by taking the immediate reconstruction results of the mapping module as input. Experimental result shows that the proposed DW-FISTA model outperforms existing state-of-art models in sparse sequence reconstruction and can ensure a faster convergence rate.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"54 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":"117204857","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