Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00100
Qianrun Chen
Mobile crowd sensing (MCS) is a computing paradigm that recruits citizens to collect and contribute sensing data from surroundings using their smart device. The incentive mechanisms and task allocation methods are critical parts that affect whether the MSC campaigns could continue gaining sensing data. In this paper, we survey the literature over the period of 2018–2020 from the state-of-the-art of incentive mechanism and task allocation method design in MCS.
{"title":"Incentive Mechanism and Task Allocation Methods for Mobile Crowd Sensing: A Survey","authors":"Qianrun Chen","doi":"10.1109/ICNISC54316.2021.00100","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00100","url":null,"abstract":"Mobile crowd sensing (MCS) is a computing paradigm that recruits citizens to collect and contribute sensing data from surroundings using their smart device. The incentive mechanisms and task allocation methods are critical parts that affect whether the MSC campaigns could continue gaining sensing data. In this paper, we survey the literature over the period of 2018–2020 from the state-of-the-art of incentive mechanism and task allocation method design in MCS.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115069277","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00032
Maofan Wang
With the growth of national strength, China's infrastructure construction capacity is growing. Traffic signal light is the soul of traffic dispatching, which can improve traffic smoothness and ensure pedestrian safety. The complicated traffic network makes China all-round, but at the same time, it is also more urgent to have more intelligent and efficient dispatching capacity. The conventional traffic signal lights are isolated and static, but traffic is complex and random. Thus, the function of traffic dispatching can be achieved, and the dynamic and intelligent management of traffic can be realized.
{"title":"Traffic Signal Control Method Based on A3C Reinforcement Learning","authors":"Maofan Wang","doi":"10.1109/ICNISC54316.2021.00032","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00032","url":null,"abstract":"With the growth of national strength, China's infrastructure construction capacity is growing. Traffic signal light is the soul of traffic dispatching, which can improve traffic smoothness and ensure pedestrian safety. The complicated traffic network makes China all-round, but at the same time, it is also more urgent to have more intelligent and efficient dispatching capacity. The conventional traffic signal lights are isolated and static, but traffic is complex and random. Thus, the function of traffic dispatching can be achieved, and the dynamic and intelligent management of traffic can be realized.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115102588","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00083
Lei Liang, Xiaolei Zhou
With the booming development of social media, many people use social software to share their life experiences and express their opinions, viewpoints and experiences on social hot spots, thus forming a huge amount of information. This paper takes microblog topic comments as the research object and makes visual analysis of microblog comments from the perspective of emotional orientation, which is of great research significance for relevant departments to timely grasp the changes in the masses' thoughts and timely control and deal with emergencies. In this study, the Bert-LSTM model was used for sentiment classification of microblog comments, and the complex and sparse data sets were visualized to convert the disordered data signals into graphic representations. Through in-depth emotional mining of public opinion comments, the importance and effectiveness of online public opinion analysis in the era of data explosion are verified.
{"title":"Research on Public Sentiment of Weibo Topics Based on Emotional Tendency-Taking “LaBiXiaoQiu Was Detained” as an Example","authors":"Lei Liang, Xiaolei Zhou","doi":"10.1109/ICNISC54316.2021.00083","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00083","url":null,"abstract":"With the booming development of social media, many people use social software to share their life experiences and express their opinions, viewpoints and experiences on social hot spots, thus forming a huge amount of information. This paper takes microblog topic comments as the research object and makes visual analysis of microblog comments from the perspective of emotional orientation, which is of great research significance for relevant departments to timely grasp the changes in the masses' thoughts and timely control and deal with emergencies. In this study, the Bert-LSTM model was used for sentiment classification of microblog comments, and the complex and sparse data sets were visualized to convert the disordered data signals into graphic representations. Through in-depth emotional mining of public opinion comments, the importance and effectiveness of online public opinion analysis in the era of data explosion are verified.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128135165","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00053
Zoubaydat Dahirou, Mao Zheng
Recently, the field of artificial intelligence has seen many advances thanks to deep learning and image processing. It is now possible to recognize images or even find objects inside an image with a standard GPU. Image processing is a recent science that aims to provide specialists from different areas, as to the general public, tools for manipulating these digital data from the real world. The detection of moving objects is a crucial step for systems based on image processing. The movements detected by the classic algorithms are not necessarily interesting for a thorough information search, and the need to distinguish the coherent movements of parasitic movements exists in most cases. In this paper we are going to use a simply webcam and YOLO algorithm for this implementation. The YOLOv3 (Version 3) model makes predictions with a single network evaluation, making this method extremely fast, running in real time with a capable GPU. From there we'll use OpenCV, Python, and deep learning to apply the YOLOv3 object to images and apply YOLOv3 to video streams.
{"title":"Motion Detection and Object Detection: Yolo (You Only Look Once)","authors":"Zoubaydat Dahirou, Mao Zheng","doi":"10.1109/ICNISC54316.2021.00053","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00053","url":null,"abstract":"Recently, the field of artificial intelligence has seen many advances thanks to deep learning and image processing. It is now possible to recognize images or even find objects inside an image with a standard GPU. Image processing is a recent science that aims to provide specialists from different areas, as to the general public, tools for manipulating these digital data from the real world. The detection of moving objects is a crucial step for systems based on image processing. The movements detected by the classic algorithms are not necessarily interesting for a thorough information search, and the need to distinguish the coherent movements of parasitic movements exists in most cases. In this paper we are going to use a simply webcam and YOLO algorithm for this implementation. The YOLOv3 (Version 3) model makes predictions with a single network evaluation, making this method extremely fast, running in real time with a capable GPU. From there we'll use OpenCV, Python, and deep learning to apply the YOLOv3 object to images and apply YOLOv3 to video streams.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414131","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00155
Qi Yang, Qunbin Chen, Pai Zhang
In this study, we prove strong stability for a typical time-varying nonlinear dynamic system in batch culture, which is hard to obtain analytical solutions and equilibrium points. To this end, firstly, we construct a linear variational system to the nonlinear dynamic system. Secondly, we give a proof that the fundamental matrix solution to this dynamic system is bounded. Combined with the above two points, the strong stability for the nonlinear dynamic system is proved.
{"title":"Strong Stability of Optimal Design for a Time-varying Dynamic System in Batch Culture","authors":"Qi Yang, Qunbin Chen, Pai Zhang","doi":"10.1109/ICNISC54316.2021.00155","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00155","url":null,"abstract":"In this study, we prove strong stability for a typical time-varying nonlinear dynamic system in batch culture, which is hard to obtain analytical solutions and equilibrium points. To this end, firstly, we construct a linear variational system to the nonlinear dynamic system. Secondly, we give a proof that the fundamental matrix solution to this dynamic system is bounded. Combined with the above two points, the strong stability for the nonlinear dynamic system is proved.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128910195","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00127
Quchao Cheng, Jiaojie Li, Guochao Shen, Qingmin Du
In this paper, a digital image soil analysis model based on machine learning is established.According to the mean value of HSV and image foreground, two algorithms, MLP and SVM, were used to predict the drug content in the same soil, which proved the accuracy of image analysis by MLP network and support vector machine. Drug content detection by image can be applied to land management, which provides a new idea and effective reference for comprehensive soil analysis in many aspects.
{"title":"Digital Image Soil Analysis based on Machine Learning","authors":"Quchao Cheng, Jiaojie Li, Guochao Shen, Qingmin Du","doi":"10.1109/ICNISC54316.2021.00127","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00127","url":null,"abstract":"In this paper, a digital image soil analysis model based on machine learning is established.According to the mean value of HSV and image foreground, two algorithms, MLP and SVM, were used to predict the drug content in the same soil, which proved the accuracy of image analysis by MLP network and support vector machine. Drug content detection by image can be applied to land management, which provides a new idea and effective reference for comprehensive soil analysis in many aspects.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997449","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00042
Xin-xing Gong
Artificial intelligence, as the main driving force of the new scientific and technological revolution, will inevitably bring some threat to the future in the process of upgrading and transformation, but more importantly, the new development opportunities. Ideological and political work has always been the “lifeline” of the work of the Party and the state, and will usher in unprecedented development opportunities in the era of artificial intelligence. Artificial intelligence as an information technology can enable ideological and political education in colleges and universities, one is to expand in time, space and situation, that is, to increase free time, expand space, and create a scene of localization to eliminate the dual opposition between the subject and object of ideological and political education and highlight its Noumenon; the other is to create a personalized education with meticulous, accurate and exquisite characteristics; and the third is to use “bit,” man-machine integration”, “Cognitive Outsourcing” realizes the co-construction, co-governance and sharing of ideological and political education resources in colleges and universities; fourth, using big data thinking, deep learning concept and machine learning. The principle of “black box” explores new laws, new ideas and new models of ideological and political education theory.
{"title":"Exploration on the Opportunity of Ideological and Political Education in the Age of Artificial Intelligence","authors":"Xin-xing Gong","doi":"10.1109/ICNISC54316.2021.00042","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00042","url":null,"abstract":"Artificial intelligence, as the main driving force of the new scientific and technological revolution, will inevitably bring some threat to the future in the process of upgrading and transformation, but more importantly, the new development opportunities. Ideological and political work has always been the “lifeline” of the work of the Party and the state, and will usher in unprecedented development opportunities in the era of artificial intelligence. Artificial intelligence as an information technology can enable ideological and political education in colleges and universities, one is to expand in time, space and situation, that is, to increase free time, expand space, and create a scene of localization to eliminate the dual opposition between the subject and object of ideological and political education and highlight its Noumenon; the other is to create a personalized education with meticulous, accurate and exquisite characteristics; and the third is to use “bit,” man-machine integration”, “Cognitive Outsourcing” realizes the co-construction, co-governance and sharing of ideological and political education resources in colleges and universities; fourth, using big data thinking, deep learning concept and machine learning. The principle of “black box” explores new laws, new ideas and new models of ideological and political education theory.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130441385","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00023
Bo Su, B. Jiang, Le Qi
Inspecting an ejection device of an aircraft has been a long-standing problem due to technical, safety and cost restraints. A novel inspection instrument was designed to meet this need using low-pressure cold gas instead of the high-pressure gas. Dynamic simulation and analysis were carried out to approximate nominal parameters and MCU+CPLD design was used to increase the clock division option of the system to adapt to different ejection velocities to reduce errors. Application feedback from the fields shows that the instrument is easy to operate and reliable, and can check the performance of many types of ejection device, which will improve reliability and accuracy of releasing, the level of protection for the ejection device and effectively guarantee flight safety and the completion of training tasks.
{"title":"A Novel Design Scheme for an Ejection Device Inspection Instrument","authors":"Bo Su, B. Jiang, Le Qi","doi":"10.1109/ICNISC54316.2021.00023","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00023","url":null,"abstract":"Inspecting an ejection device of an aircraft has been a long-standing problem due to technical, safety and cost restraints. A novel inspection instrument was designed to meet this need using low-pressure cold gas instead of the high-pressure gas. Dynamic simulation and analysis were carried out to approximate nominal parameters and MCU+CPLD design was used to increase the clock division option of the system to adapt to different ejection velocities to reduce errors. Application feedback from the fields shows that the instrument is easy to operate and reliable, and can check the performance of many types of ejection device, which will improve reliability and accuracy of releasing, the level of protection for the ejection device and effectively guarantee flight safety and the completion of training tasks.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130521156","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00171
Chiyuan Qu, Zhuhao Lu, Tianyun Hu
Object detection technique is adopted in fishery, that is detecting fish. We choose to use YOLO v4, a new and efficient target detection network for sampling detection. Manual counting of fish is prone to deviations and costs a lot of manpower. Using YOLO v4 to detect fish can improve the working efficiency of the fishery and reduce management costs. We used the pictures of fishes taken from the videos taken in the pipeline of Qiandao Lake fishery. Then we preprocessed the pictures, enriched the data set and use them for training to achieve the engineering of fish detection. Finally, the target recognition accuracy of the training is over 85%, and the fps is over 14. It can realize the function of real-time detection on the basis of accurately and accurately detecting fish.
{"title":"Research on Intelligent Management of Fishing Ground Based on Target Detection","authors":"Chiyuan Qu, Zhuhao Lu, Tianyun Hu","doi":"10.1109/ICNISC54316.2021.00171","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00171","url":null,"abstract":"Object detection technique is adopted in fishery, that is detecting fish. We choose to use YOLO v4, a new and efficient target detection network for sampling detection. Manual counting of fish is prone to deviations and costs a lot of manpower. Using YOLO v4 to detect fish can improve the working efficiency of the fishery and reduce management costs. We used the pictures of fishes taken from the videos taken in the pipeline of Qiandao Lake fishery. Then we preprocessed the pictures, enriched the data set and use them for training to achieve the engineering of fish detection. Finally, the target recognition accuracy of the training is over 85%, and the fps is over 14. It can realize the function of real-time detection on the basis of accurately and accurately detecting fish.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130607926","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}
Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00086
X. Ye, Yanmei Wang, Zhichun Jia
For web services, QoS (Quality of Service, quality of service) is an important indicator for judging whether a web service is efficient. How to better predict the QoS value of the service to make appropriate service recommendations is the entire recommendation system and Issues that are being discussed in the service forecasting academia. At the same time, the timeliness and time relevance of QoS values are also affecting the prediction accuracy of Web services. A large amount of QoS data has potentially time-related attributes. This provides a new inspiration and thinking for service forecasting. Add the time characteristics of the data to the learning of the predictive model. Inspired by these factors, this paper proposes a deep neural network combination model that is sensitive to the time characteristics of QoS. At the same time, based on the final experimental results, the model proposed in this paper has obvious effects on the prediction of QoS values with time attributes.
对于web服务来说，QoS (Quality of Service，服务质量)是判断web服务是否高效的重要指标。如何更好地预测服务的QoS值，做出合适的服务推荐，是整个推荐系统和服务预测学术界正在讨论的问题。同时，QoS值的时效性和时间相关性也影响着Web服务的预测精度。大量的QoS数据具有潜在的时间相关属性。这为服务预测提供了新的启示和思路。将数据的时间特征加入到预测模型的学习中。受这些因素的启发，本文提出了一种对QoS时间特性敏感的深度神经网络组合模型。同时，根据最终的实验结果，本文提出的模型对具有时间属性的QoS值的预测效果明显。
{"title":"Web Service Quality Prediction Method Based on Recurrent Neural Network","authors":"X. Ye, Yanmei Wang, Zhichun Jia","doi":"10.1109/ICNISC54316.2021.00086","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00086","url":null,"abstract":"For web services, QoS (Quality of Service, quality of service) is an important indicator for judging whether a web service is efficient. How to better predict the QoS value of the service to make appropriate service recommendations is the entire recommendation system and Issues that are being discussed in the service forecasting academia. At the same time, the timeliness and time relevance of QoS values are also affecting the prediction accuracy of Web services. A large amount of QoS data has potentially time-related attributes. This provides a new inspiration and thinking for service forecasting. Add the time characteristics of the data to the learning of the predictive model. Inspired by these factors, this paper proposes a deep neural network combination model that is sensitive to the time characteristics of QoS. At the same time, based on the final experimental results, the model proposed in this paper has obvious effects on the prediction of QoS values with time attributes.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633608","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}
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