2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第9页

Adaptive Weight of Unreliable Relation Module for Semi-supervised Multi-label Image Recognition 半监督多标签图像识别中不可靠关系模块的自适应权重

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016315

Ziyuan Wang, Zhen Zhao, Lei Qi, Yinghuan Shi, Yang Gao

{"title":"Adaptive Weight of Unreliable Relation Module for Semi-supervised Multi-label Image Recognition","authors":"Ziyuan Wang, Zhen Zhao, Lei Qi, Yinghuan Shi, Yang Gao","doi":"10.1109/ccis57298.2022.10016315","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016315","url":null,"abstract":"Semi-supervised multi-label classification is a challenging task due to the insufficient training guidance and unknown label co-occurrence probabilities. For papers in recent years, relation modules are widely utilized to explore the potential label relationships, but the severe frequency-biased issue between a global relationship and local images significantly degrades their effectiveness. Besides, the difference in data distribution between training and testing sets further affects the performance, especially when the labeled data is limited. To address these problems, we propose a simple selective relation module to learn an adaptive weight of relation module for each image and enforce the consistency between relation-based predictions and initial predictions. In addition, we exploit the augmentation-based consistency loss to generate more confident relation-based pseudo-labels and more robust relation-importance predictions. Our methods can be added to a variety of relation-based multi-label classification methods and we show our improvements in the classification accuracy on Pascal VOC dataset.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811482","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

Visual illusion cognition dataset construction and recognition performance by deep neural networks 视觉错觉认知数据集构建及深度神经网络识别性能研究

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016369

Tingting Li, Fanyu Wang, Ying Zhou, Zhenping Xie

引用次数: 0

Adversarial Guided Gradient Estimation Hashing for Cross-modal Retrieval 面向跨模态检索的对抗导向梯度估计哈希

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016424

Kangkang Lu, M. Liang, Zhe Xue, Xiaowen Cao, Mengran Yin, Zehua Zhao

{"title":"Adversarial Guided Gradient Estimation Hashing for Cross-modal Retrieval","authors":"Kangkang Lu, M. Liang, Zhe Xue, Xiaowen Cao, Mengran Yin, Zehua Zhao","doi":"10.1109/CCIS57298.2022.10016424","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016424","url":null,"abstract":"Due to low storage cost and fast query speed, deep hashing methods are widely used in cross-modal retrieval. However, the “heterogeneous gap” between multi-modal data is still a challenge. Moreover, a major difficulty in deep hashing lies in the discrete constraints imposed on the network output. Existing solutions usually use relaxation techniques, but this inevitably produces quantization error, leading to sub-optimal hash code. In this paper, we propose Adversarial Guided Gradient Estimation Hashing (AGEH). Firstly, in order to bridge the heterogeneous gap between different modal data, a cross-modal adversarial feature learning network is constructed to learn cross-modal semantic associations. Secondly, to solve the discrete optimization problem of hash code, we propose a hashing optimization strategy based on gradient estimation for sign function, which strictly uses sign function to maintain discrete constraints in forward propagation, while in back propagation, the gradient is directly transmitted to the previous layer and thus avoid quantization error. Extensive experiments conducted on two cross-modal benchmark datasets show that our proposed AGEH outperforms several state-of-the-art methods.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"25 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130700346","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

Path Planning Considering Time-Varying and Uncertain Movement Speed in Multi-Robot Automatic Warehouses: Problem Formulation and Algorithm 考虑时变和不确定移动速度的多机器人自动仓库路径规划:问题表述与算法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016376

Jing-ya Chen, Wei Chen, Jing Li, Xiguang Wei, Wenzhe Tan, Zuo‐Jun Max Shen, Hongbo Li

{"title":"Path Planning Considering Time-Varying and Uncertain Movement Speed in Multi-Robot Automatic Warehouses: Problem Formulation and Algorithm","authors":"Jing-ya Chen, Wei Chen, Jing Li, Xiguang Wei, Wenzhe Tan, Zuo‐Jun Max Shen, Hongbo Li","doi":"10.1109/CCIS57298.2022.10016376","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016376","url":null,"abstract":"Path planning in the multi-robot system refers to calculating a set of actions for each robot, which will move each robot to its goal without conflicting with other robots. Lately, the research topic has received significant attention for its extensive applications, such as airport ground, drone swarms, and automatic warehouses. Despite these available research results, most of the existing investigations are concerned with the cases of robots with a fixed movement speed without considering uncertainty. Therefore, in this work, we study the problem of path-planning in the multi-robot automatic warehouse context, which considers the time-varying and uncertain robots’ movement speed. Specifically, the path-planning module searches a path with as few conflicts as possible for a single agent by calculating traffic cost based on customarily distributed conflict probability and combining it with the classic $mathrm{A}^{*}$ algorithm. However, this probability-based method cannot eliminate all conflicts, and speed’s uncertainty will constantly cause new conflicts. As a supplement, we propose the other two modules. The conflict detection and re-planning module chooses objects requiring re-planning paths from the agents involved in different types of conflicts periodically by our designed rules. Also, at each step, the scheduling module fills up the agent’s preserved queue and decides who has a higher priority when the same element is assigned to two agents simultaneously. Finally, we compare the proposed algorithm with other algorithms from academia and industry, and the results show that the proposed method is validated as the best performance.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122234102","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

QoE-Guaranteed Heterogeneous Task Offloading with Deep Reinforcement Learning in Edge Computing 边缘计算中基于深度强化学习的qos保证异构任务卸载

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016367

Zhiwen Zhou, Yingbo Wu, Jiaxin Hou

{"title":"QoE-Guaranteed Heterogeneous Task Offloading with Deep Reinforcement Learning in Edge Computing","authors":"Zhiwen Zhou, Yingbo Wu, Jiaxin Hou","doi":"10.1109/CCIS57298.2022.10016367","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016367","url":null,"abstract":"In edge-enabled Internet-of-Things (IoT), various IoT applications commonly generate heterogeneous tasks. Existing heterogeneous task offloading methods are developed to guarantee the Quality-of-Service (QoS), instead of the Quality-of-Experience (QoE) from the user’s perspective. However, QoE-guaranteed heterogeneous task offloading can significantly improve the actual user experiences of IoT applications. In this paper, the heterogeneity of offloading tasks is categorized as delay-aware, energy-aware, and privacy-aware. We define a novel QoE metric and adopt a statistical policy to relax the deterministic constraints of heterogeneous tasks to improve the offloading success rate. Furthermore, we formulate the QoE-guaranteed heterogeneous tasks offloading problem as a Mixed-integer Nonlinear Programming (MINLP) problem. Conventional numerical optimization methods are inefficient in solving such problems, therefore, we apply a Deep Reinforcement Learning (DRL) algorithm to make the optimal offloading decision that satisfies the offloading intention of the task. Experiment results show that our algorithm effectively guarantees the global QoE performance and improves the offloading success rate of heterogeneous tasks.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116099043","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

Stacked Ensemble of Metamodels for Expensive Global Optimization 昂贵全局优化的元模型叠加集成

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016330

Ziliang Miao, Buwei He, Hubocheng Tang, Jixiang Chen, Zhenkun Wang

引用次数: 0

Kernel Least Mean Square With Maximum Correntropy Criterion 具有最大相关系数准则的核最小均方

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016417

Yawen Li, Wenling Li, Zhe Xue, Ang Li

引用次数: 0

SDW-DPC: An Advanced Clustering Algorithm by Searching Density Peaks using Standard Deviation Weighted Distance SDW-DPC:一种利用标准差加权距离搜索密度峰值的高级聚类算法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016309

Juanying Xie, Xingli Liu, Mingzhao Wang, Wenjie Zhang

{"title":"SDW-DPC: An Advanced Clustering Algorithm by Searching Density Peaks using Standard Deviation Weighted Distance","authors":"Juanying Xie, Xingli Liu, Mingzhao Wang, Wenjie Zhang","doi":"10.1109/CCIS57298.2022.10016309","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016309","url":null,"abstract":"DPC (clustering by fast search and find of density peaks) algorithm is an ingenious and efficient clustering algorithm that can discover cluster centers via its very novelty decision graph and subsequently achieve the clustering of a dataset efficiently via its innovative one-step assignment strategy. However, DPC algorithm has its inborn shortcomings, such as its “Domino Effect” that once a point is assigned to an error cluster, then there will be many subsequent points being assigned to error clusters, resulting in a poor clustering. This shortcoming in part due to its one-step assignment, and in part due to its distance metric. DPC uses the Euclidean distance to calculate the distance between points. The Euclidean distance takes it as default that each feature does equal contribute to the distance between points, but in practice, each feature does not make equal contribute to the distance. To address this shortcoming, this paper proposes a standard deviation weighted distance instead of the Euclidean distance used in DPC algorithm. This innovative distance weights a feature using the standard deviation of the feature on all points from a dataset, so that the distance between points embodies the specific contribution of the feature to the distance. The very efficient one-step assignment strategy is inherited. Therefore, we developed the advanced PDC clustering algorithm which is referred to as SDW-DPC (Standard Deviation Weighted Distance based Density Peaks Clustering) algorithm. Extensive experiments on synthetic datasets and real-world datasets from UCI machine learning repository demonstrate that our SDW-DPC outperforms the original DPC, and other famous benchmark clustering algorithms including AP, DBSCAN and K-means in terms of clustering accuracy (Acc), adjusted mutual information (AMI), and adjusted rand index (ARI).","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133345411","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 deep learning algorithm for stock selection based on multi-factor anomaly detection 基于多因素异常检测的深度学习选股算法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016432

Jun-Cheng Chen, Zhen Li, Xiaoyun Cai, Zhi Cai, Wei-Wei Wang

{"title":"A deep learning algorithm for stock selection based on multi-factor anomaly detection","authors":"Jun-Cheng Chen, Zhen Li, Xiaoyun Cai, Zhi Cai, Wei-Wei Wang","doi":"10.1109/ccis57298.2022.10016432","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016432","url":null,"abstract":"In recent years, quantitative investment has been a hot spot in the development of the financial market. Quantitative stock selection is the most crucial part of quantitative investment. It is of great significance to study how to select high-quality stocks from thousands of stocks, bring them into the stock pool and allocate assets. Various machine learning and deep learning algorithms have been used in this research. This paper proposes a new stock selection strategy for multi-factor anomaly detection based on variational auto-encoder. First, we select factors from three aspects: fundamental, technical, and capitalization. Then, unsupervised anomaly detection is performed on the multivariate time series data based on variational auto-encoder to obtain the anomaly scores of the factors, and get the abnormal result by comparing it with the threshold. Finally, the abnormal results are used to select stocks combined with the trend of the selected stocks. We apply the model to four groups of stocks belonging to SCI300, SSE 50, SZSI and CSI500 respectively, and evaluate the performance compared with the Buy&Hold strategy, Traditional multi-factor model stock selection strategy, AdaBoost machine learning stock selection strategy. The experimental results show that the model can identify “good” stocks from the sample, and the performance of the selected portfolio is better than the benchmarks test.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122765131","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

Robust-STP: A Robust Seasonal-trend Decomposition Method for Partial Periodic Time Series 鲁棒stp:部分周期时间序列的鲁棒季节趋势分解方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016327

Haidong Xu, Xiaoxia Zhang, Dong Liang, Guoyin Wang

{"title":"Robust-STP: A Robust Seasonal-trend Decomposition Method for Partial Periodic Time Series","authors":"Haidong Xu, Xiaoxia Zhang, Dong Liang, Guoyin Wang","doi":"10.1109/ccis57298.2022.10016327","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016327","url":null,"abstract":"The extraction of trend and seasonal components from time series is essential for tasks such as forecasting and anomaly detection of the data. The existing decomposition methods of time series mainly concentrate on full-period time series, that is, the periodicity of data runs through the whole time series, less effort has been paid on those kinds of time series that with the mixture of periodicity and aperiodicity. However, in the real world, much of the time series appears mostly in a mixture of periodicity and aperiodicity. Based on this consideration, in this paper, we propose a novel robust seasonal-trend decomposition method for partially periodic time series, short for Robust-STP, to fill this research gap. Firstly, we use bilateral filtering and least absolute deviation loss with regularizations to remove noise and relative trends in the data. Secondly, a sliding window based on the dynamic time warping algorithm is employed to locate the interval points between periodic and aperiodic data. Finally, seasonal and trend filters are imposed to extract the final seasonal and trend components, respectively. Experimental results on synthetic and real datasets are proved to the effectiveness of Robust-STP on partial periodic time series.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124794108","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