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2021 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献

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Twin Delayed Multi-Agent Deep Deterministic Policy Gradient 双延迟多智能体深度确定性策略梯度
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687069
Mengying Zhan, Jinchao Chen, Chenglie Du, Yuxin Duan
{"title":"Twin Delayed Multi-Agent Deep Deterministic Policy Gradient","authors":"Mengying Zhan, Jinchao Chen, Chenglie Du, Yuxin Duan","doi":"10.1109/PIC53636.2021.9687069","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687069","url":null,"abstract":"Recently, reinforcement learning has made remarkable achievements in the fields of natural science, engineering, medicine and operational research. Reinforcement learning addresses sequence problems and considers long-term returns. This long-term view of reinforcement learning is critical to find the optimal solution of many problems. The existing multi- agent reinforcement learning algorithms have the problem of overestimation in estimating the Q value. Unfortunately, there have not been many studies on overestimation of agent reinforcement learning, which will affect the learning efficiency of reinforcement learning. Based on the traditional multi-agent reinforcement learning algorithm, this paper improves the actor network and critic network, optimizes the overestimation of Q value and adopts the update delayed method to make the actor training more stable. In order to test the effectiveness of the algorithm structure, the modified method is compared with the traditional MADDPG, DDPG and DQN methods in the simulation environment.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123463606","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: 3
Recommendation Method for Attractive Destinations for Individual Tourists Using Profile Data 基于个人资料的个性化旅游目的地推荐方法
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687061
Minghao Li, Jun Sasaki
{"title":"Recommendation Method for Attractive Destinations for Individual Tourists Using Profile Data","authors":"Minghao Li, Jun Sasaki","doi":"10.1109/PIC53636.2021.9687061","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687061","url":null,"abstract":"Personalized tours have become popular worldwide. However, it is not easy to recommend destinations that are appropriate for individual tourists. This study examines a highly accurate recommendation method using two indexes: an objective index that judges the adaptability between a tourist’s profile and a destination; and a subjective index that judges attractiveness for the tourist. We tested the method using data from tourism websites and a questionnaire survey. We found that the method was effective in identifying adaptive and attractive tourist groups for well-known destinations in Iwate Prefecture, Japan.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116224866","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
Improve Exploration of Arithmetic Optimization Algorithm by Opposition-based Learning 基于对立学习改进算法优化算法的探索
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687010
Xia Lin, Haomiao Li, Xin Jiang, Yuchao Gao, Jinran Wu, Yang Yang
{"title":"Improve Exploration of Arithmetic Optimization Algorithm by Opposition-based Learning","authors":"Xia Lin, Haomiao Li, Xin Jiang, Yuchao Gao, Jinran Wu, Yang Yang","doi":"10.1109/PIC53636.2021.9687010","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687010","url":null,"abstract":"An improved version of the arithmetic optimization algorithm (AOA) based on the opposition-based learning (OBL) strategy called OBLAOA is proposed in this paper. The proposed OBLAOA algorithm consists of two stages, and in the second stage we adds OBL to update the AOA population in each iteration. The improved AOA is compared with the original AOA by using 12 benchmark functions in different dimensions to validate the improvement on exploration with the OBL. Eventually ,we get a conclusion that the OBLAOA is committed to take both candidate solutions and their opposite solutions into consideration, which shows greater opportunity to reach the global optimal and faster convergence acceleration than AOA.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124228186","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
Research on Stratospheric Communication Effectiveness Evaluation 平流层通信效能评估研究
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687087
Yubing Men, Jian-gang Zheng
{"title":"Research on Stratospheric Communication Effectiveness Evaluation","authors":"Yubing Men, Jian-gang Zheng","doi":"10.1109/PIC53636.2021.9687087","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687087","url":null,"abstract":"As a new communication means, stratospheric communication can effectively cover the shortage of short wave communication and satellite communication. At present, there is no research of stratospheric communication effectiveness evaluation, while effectiveness evaluation is an important way to evaluate means of communication. Therefore, an effective evaluation of stratospheric communication effectiveness is very important for the development of long-distance communication. The key factor indexes affecting the effectiveness of stratospheric communication are analyzed by grey analytic hierarchy process. The effectiveness evaluation index system is formed, and the specific quantitative method of the index is given. According to the characteristics of stratospheric communication, a specific case is designed for analyzing the effectiveness evaluation of stratospheric communication.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125371301","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
Recognition of Usual Similar Activities of Dementia Patients via Smartwatches Using Supervised Learning 通过使用监督学习的智能手表识别痴呆症患者通常的类似活动
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687025
Sergio Staab, Johannes Luderschmidt, Ludger Martin
{"title":"Recognition of Usual Similar Activities of Dementia Patients via Smartwatches Using Supervised Learning","authors":"Sergio Staab, Johannes Luderschmidt, Ludger Martin","doi":"10.1109/PIC53636.2021.9687025","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687025","url":null,"abstract":"Currently, about 46.8 million people worldwide have dementia. More than 7.7 million new cases occur every year. Causes and triggers of the disease are currently unknown and a cure is not available. This makes dementia, along with cancer, one of the most dangerous diseases in the world. In the field of dementia care, this work attempts to use machine learning to classify the activities of individuals with dementia in order to track and analyze disease progression and detect disease-related changes as early as possible. In collaboration with two care communities, exercise data is measured using the Apple Watch Series 6. Consultation with several care teams that work with dementia patients on a daily basis revealed that many dementia patients wear watches.In this project, data from the aforementioned sensors is sent to the database at 20 data packets per second via a socket. DecisionTreeClassifier, KNeighborsClassifier, Logistic Regression, Fast Forest, Support Vector Machine, and Multilayer Perceptron classification algorithms are used to gain knowledge about locating, providing, and documenting motor skills during the course of dementia. The performance of the aforementioned algorithms for three similar activities of the dementia patients – writing, drinking and eating – will be investigated. The aim is to show the performance with which the activities can be recognized and how this knowledge can be used to support dementia documentation by nursing staff.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125182805","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: 6
Multi-Round Recommendations for Stable Groups 稳定群体的多轮建议
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687062
Ilmo Heiska, K. Stefanidis
{"title":"Multi-Round Recommendations for Stable Groups","authors":"Ilmo Heiska, K. Stefanidis","doi":"10.1109/PIC53636.2021.9687062","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687062","url":null,"abstract":"Recommender systems have been used for suggesting the most suitable products and services for users in diverse scenarios. More recently, the need for making recommendations for groups of users has become increasingly relevant. In addition, there are applications in which recommendations are required in a consecutive sequence. Group recommendations present a challenge for recommender systems: how to balance the preferences of the individual members of a group. On the other hand, when making recommendations for a group for multiple rounds, a recommender has a possibility to dynamically try to balance the preference differences between the group members. This paper introduces two novel methods for multi-round group recommendation scenarios: the adjusted average aggregation method and the average-min-disagreement aggregation method. Both methods aim to provide a group with highly relevant results for the group, while remaining fair for all group members. We experimentally evaluate our approach for groups with different characteristics and show that our methods outperform baseline solutions in all scenarios.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125512760","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
Generation of Virtual Test Scenarios for Training and Validation of AI-based Systems 基于人工智能系统的训练和验证虚拟测试场景的生成
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687075
Ulrich Dahmen, T. Osterloh, J. Roßmann
{"title":"Generation of Virtual Test Scenarios for Training and Validation of AI-based Systems","authors":"Ulrich Dahmen, T. Osterloh, J. Roßmann","doi":"10.1109/PIC53636.2021.9687075","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687075","url":null,"abstract":"Technologies in the field of artificial intelligence (AI) are increasingly used in a wide variety of industries. However, in addition to their considerable potential, it is important to note that the behavior of an AI-based system cannot be predicted based on its internal architecture, and thus its correct behavior cannot be guaranteed. Instead, both the performance and the reliability depend on the amount and quality of training data. The greater the complexity of a task required from the AI, the less likely it is that the necessary training and validation data can be collected through real measurement series in practice. A popular example is the situation awareness required for autonomous driving, which requires an ever-increasing amount of kilometers to be driven. Therefore, people are now gradually shifting to the inclusion of synthetic training data generated by simulation. This is where this paper comes in. We present a concept for a flexible generation of virtual test scenarios based on a systematic use of digital twins and virtual testbeds, allowing to generate training and validation data for AI-based systems in appropriate quantity, quality, and time.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127367222","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: 3
Lion Swarm Optimization Based on Balanced Local and Global Search with Different Distributions 基于局部与全局平衡搜索的不同分布狮群优化
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687052
Keqin Jiang, M. Jiang
{"title":"Lion Swarm Optimization Based on Balanced Local and Global Search with Different Distributions","authors":"Keqin Jiang, M. Jiang","doi":"10.1109/PIC53636.2021.9687052","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687052","url":null,"abstract":"In view of the shortcomings of the basic lion swarm optimization, which is prone to local optimization and low convergence accuracy in partial optimization, this paper proposes a lion swarm optimization based on balanced local and global search with different distributions. The improved algorithm adds chaos search and different distributed perturbation strategies to the positions of lions in the earlier stage, which improves the optimization efficiency of the algorithm in the optimization process. These disturbance strategies include variations based on Cauchy mutation, t probability distribution, and levy flight. The simulation results of the test functions show that the optimization accuracy of the improved algorithm is much higher than that of the basic lion swarm optimization. The improved algorithm effectively prevents the swarm optimization from easily falling into the local optimization value in the extremely difficult optimization functions.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123113963","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
Text Stance Detection Based on Deep Learning 基于深度学习的文本姿态检测
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687002
Xu Zhang, Chunyang Liu, Z. Gao, Yue Jiang
{"title":"Text Stance Detection Based on Deep Learning","authors":"Xu Zhang, Chunyang Liu, Z. Gao, Yue Jiang","doi":"10.1109/PIC53636.2021.9687002","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687002","url":null,"abstract":"Stance analysis refers to the usage of natural language processing and data mining technology to determine the stance tendency of a specific target topic in text. The current stance detection research faces the problem of the relationship between the topic target information and the stance text information being not fully tapped, which affects the text stance analysis task performance in social media. In view of this, based on the existing deep learning framework, combined with the topic target information in stance analysis, this study proposes a stance analysis model based on a convolutional neural network under the independent encoding of the topic target information and the condition encoding of the topic target information. The SemEval2016 English Dataset and the NLPCC2016 Chinese dataset are used herein separately to conduct the experiments. The experimental results show that the model is effective in the stance detection task of a specific topic target.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554055","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 of SPOC Online Learning Behavior Analysis Based on RFT 基于RFT的SPOC在线学习行为分析研究
2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687031
Hongxia Wang
{"title":"Research of SPOC Online Learning Behavior Analysis Based on RFT","authors":"Hongxia Wang","doi":"10.1109/PIC53636.2021.9687031","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687031","url":null,"abstract":"It has a direct impact on the learning effect that the occurrence of online learning behavior. The SPOC online learning platform Superstar (Chao Xing) used by our college is taken as an example to conduct this research. Student behavior data participating in SPOC platform online learning is collected, including the length of viewing resources, the number of times to log in the platform, and frequency, etc. The classical RFM model in the big data customer relationship management is improved according to actual needs based on the large amount of data in the online learning platform. And the SPOC online learning behavior analysis model based on RFM is proposed, that is RFT. Empirical analysis on SPOC platform online learning behavior is conducted with the RFT model. Students' learning habits and external influencing factors can be known through empirical research. In the experiment, the data is processed by attribute specification and standardization. Then the students are gathered using the K-Means clustering algorithm. And the R, F, and T indicators are visualized and analyzed through the radar chart and histogram.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131173038","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
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