2022 8th International Conference on Systems and Informatics (ICSAI)最新文献

Byzantine Consensus Based on Modified Treap Topology 基于改进Treap拓扑的拜占庭共识

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005475

Yan Gao, Qiang Wang, Liang Cai

{"title":"Byzantine Consensus Based on Modified Treap Topology","authors":"Yan Gao, Qiang Wang, Liang Cai","doi":"10.1109/ICSAI57119.2022.10005475","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005475","url":null,"abstract":"In the traditional Byzantine consensus schemes, the efficiency and throughput of the whole topology will be limited by transmission delay in nodes, which depends on the complexity of message and bandwidth constraints. This paper proposes a Byzantine consensus scheme based on weight modified Treap topology that reduces the complexity and delays of message aggregation by quickly constructing and maintaining Treap node tree. We have the master node organize other active nodes into a balanced tree rooted at itself to allocate communication and computing costs. We propose a failure detection mechanism for Treap that makes the master node record the weights of the nodes for each of its direct child nodes. The master node establishes a delay function to set the weight of each node based on the delay between receiving PREPARE message and completing its own calculation to sending its share. With a node's weight over threshold, it is assumed that the node has failed and been marked. The failure detection mechanism of the marked nodes starts to maintain availability by replacing the replication of the marked nodes. It also prevents the parent node from doing evil by authorizing nodes to mark only its direct child node. We only update the Treap topology on a regular basis or in case of failure to reduce the update overhead.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129140060","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

Short text classification method with dual channel hypergraph convolution networks 基于双通道超图卷积网络的短文本分类方法

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005421

Liu Jin, Zhaochun Sun, Huifang Ma

{"title":"Short text classification method with dual channel hypergraph convolution networks","authors":"Liu Jin, Zhaochun Sun, Huifang Ma","doi":"10.1109/ICSAI57119.2022.10005421","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005421","url":null,"abstract":"In some fields such as e-commerce and social media platforms and sentiment analysis, efficient short text classification is crucial to enable users to locate pertinent information effectively. Along with the increasing number of short texts, classifying short texts with brief contents and sparse features has become a major research topic in recent years. Towards this end, a short text classification method based on a dual channel hypergraph convolutional network is proposed to flexibly capture the complex higher-order relationships among short texts and words. Specifically, our method firstly models the pre-processed short text data into short text hypergraph and short text association graph; secondly, two different short text feature representations are learned via a dual channel hypergraph convolutional network and fused by an attention network to enhance the short text embedding; at last, a classification model is adopted to perform short text classification. Extensive experimental results indicate that the method has superior short text classification effect and stability compared with the existing model, which has better performance among comparable short text classification models.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116339171","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

Students’ Classroom Behavior Detection Based on Human-Object Interaction Model 基于人-物交互模型的学生课堂行为检测

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005457

Yonghe Zhang, Wenjiao Qu, Guocheng Zhong, Yundan Xiao

引用次数: 0

End-to-End Efficient Indoor Navigation with Optical Flow 基于光流的端到端高效室内导航

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005455

Boran Wang, Minghao Gao

{"title":"End-to-End Efficient Indoor Navigation with Optical Flow","authors":"Boran Wang, Minghao Gao","doi":"10.1109/ICSAI57119.2022.10005455","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005455","url":null,"abstract":"There has been a recent interest in employing reinforcement learning for training end-to-end goal-driven robot navigation policies. However, implementing reinforcement learning in end-to-end navigation may result in inefficient policies that exhibit redundant turning actions when attempting to avoid obstacles. This work proposes a two branches network to learn efficient policies with less turning action when robots cross the obstacles. We first employ supervised learning to train a robot action classification network with optical flow. We then combine this classifier with an RGBD optical encoder to develop an action-decision network. Ultimately, we evaluate our approach in a visually realistic simulation environment. The results show that our method can reduce unnecessary steering actions and improve efficiency while ensuring navigation capabilities. We further show that our approach can reduce energy consumption during navigation and extend the robot's work time. Experiment results in the iGibson® simulator over hand-made paths reveal that our method can reduce 13.1% of the action number in the training set and 12.9% in the testing set compared with the baseline approaches. It also can reduce 8.3% energy consumption in the training set and 9.6% in the testing set and only has a 4.2% and 8.1% difference compared with the human path.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133734723","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 Approach for Trajectory Simplification Based on Essential Inflection Point Extraction 一种基于本质拐点提取的有效轨迹简化方法

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005476

Yi Zhang, Liang Zhou

引用次数: 0

An Image Encryption Scheme with the Associated Thumbnail 带有关联缩略图的图像加密方案

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005351

Shanwu Shao, Ji Li, Ping Shao, Xiangyuan Zhu

引用次数: 0

Two-dimension Super-resolution Range Doppler Imaging in Automotive Radar 二维超分辨距离多普勒成像在汽车雷达中的应用

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005501

Jieru Ding, Min Wang, Xinghui Wu, Zhiyi Wang

引用次数: 0

Semantic Assisted LiDAR Odometry with Loop Closure in Large Scale Urban Environment 大规模城市环境中具有闭环的语义辅助激光雷达里程测量

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005509

Jiaye Lin, Yanjie Liu

引用次数: 0

Multi-channel Contrastive Learning for Sequential Recommendation 序列推荐的多通道对比学习

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005401

Quanhong Tian

{"title":"Multi-channel Contrastive Learning for Sequential Recommendation","authors":"Quanhong Tian","doi":"10.1109/ICSAI57119.2022.10005401","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005401","url":null,"abstract":"The purpose of Sequential Recommendation (SR) is to recommend the next commodities that a user wants to buy based on their historical interaction sequence. The current approach for SR focuses only on mining the user’s interest preferences, while they all fail to consider the influence of item prices on users’ purchase decisions and suffer from the data sparsity problem. In this paper, a Multi-channel Contrastive Learning method for SR (MCLSR) is proposed, which can effectively extract users’ interest preferences and price preferences and alleviate the sparsity issues. Specifically, first, a heterogeneous knowledge graph is constructed from all interaction sequence and the item attribute (i.e., item price and category) by us. Then, we leverage a heterogeneous graph neural network mechanism to learn user, item, and price node embeddings. Next, users’ price preferences and interest preferences are extracted by an attention network. Finally, a multi-channel contrastive learning mechanism is employed to build price and interest preferences’ relations and generate high-quality recommendation results. Experiments on both real datasets show that MCLSR obtains more sophisticated performance than the existing baseline.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121269275","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

Application Research of the XGBoost-SVM Combination Model in Quantitative Investment Strategy XGBoost-SVM组合模型在定量投资策略中的应用研究

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005355

Hongxing Zhu, Anmin Zhu

引用次数: 0