Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.
{"title":"A Fast Density Peak Clustering Method with Autoselect Cluster Centers","authors":"Zhihe Wang, Yongbiao Li, Hui Du, Xiaofen Wei","doi":"10.1155/2022/4176101","DOIUrl":"https://doi.org/10.1155/2022/4176101","url":null,"abstract":"Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"34 1","pages":"4176101:1-4176101:13"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76092080","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}
With the development of new satellite payload technology, in order to improve the utilization of system resources, research is based on software-defined network (SDN) and network function virtualization (NFV) gateway architecture. Based on this architecture, the system realizes global resource management and overall data distribution, which can solve the problem of resource allocation and maximum/minimum rate guarantee between different VNO terminals under different beams, different gateways, and different satellites. For this, a global bandwidth management method can be used which is mainly a process of management to control the traffic on a communication link. The proposed global resource management and control method can be based on the rate guarantee value of the VNO/terminal configured in the system as the basic limiting condition and reallocate the rate guarantee value limiting parameter according to the resource application status of the online terminal. The method can maximize the resource utilization of the entire satellite communication system and satisfy the resource request of the user terminal as much as possible.
{"title":"Virtualized High Throughput Satellite Gateway with a Global Bandwidth Management Method","authors":"Haike Liu, Huajian Zhang, Kai Yang, Jiali Li","doi":"10.1155/2022/6257885","DOIUrl":"https://doi.org/10.1155/2022/6257885","url":null,"abstract":"With the development of new satellite payload technology, in order to improve the utilization of system resources, research is based on software-defined network (SDN) and network function virtualization (NFV) gateway architecture. Based on this architecture, the system realizes global resource management and overall data distribution, which can solve the problem of resource allocation and maximum/minimum rate guarantee between different VNO terminals under different beams, different gateways, and different satellites. For this, a global bandwidth management method can be used which is mainly a process of management to control the traffic on a communication link. The proposed global resource management and control method can be based on the rate guarantee value of the VNO/terminal configured in the system as the basic limiting condition and reallocate the rate guarantee value limiting parameter according to the resource application status of the online terminal. The method can maximize the resource utilization of the entire satellite communication system and satisfy the resource request of the user terminal as much as possible.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"235 1","pages":"6257885:1-6257885:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74730686","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}
With the continuous advancement of science and technology and the rapid development of robotics, it has become an inevitable trend for domestic robots to enter thousands of households. In order to solve the inconvenience problem of the elderly and people with special needs, because the elderly and other people in need may need the help of domestic robots due to inconvenient legs and feet, the research of the robot target position based on monocular stereo vision and the understanding of the robot NAO are very important. Research and experiments are carried out on the target recognition and positioning in the process of NAO robot grasping. This paper proposes a recognition algorithm corresponding to quantitative component statistical information. First, extract the area of interest that contains the purpose from the image. After that, to eliminate interference in various fields and achieve target recognition, the robot cameras have almost no common field of view and can only use one camera at the same time. Therefore, this article uses the monocular vision principle to locate the target, and the detection algorithm is based on the structure of the robot head material, establishes the relationship between the height change of the machine head and the tilt angle, and improves the monocular vision NAO robot detection algorithm. According to experiments, the accuracy of the robot at close range can be controlled below 1 cm. This article completes the robot’s grasping and transmission of the target. About 80% of the external information that humans can perceive comes from vision. In addition, there are advantages such as high efficiency and good stability.
{"title":"Target Positioning and Grasping of NAO Robot Based on Monocular Stereo Vision","authors":"Hongbin Chen","doi":"10.1155/2022/4207694","DOIUrl":"https://doi.org/10.1155/2022/4207694","url":null,"abstract":"With the continuous advancement of science and technology and the rapid development of robotics, it has become an inevitable trend for domestic robots to enter thousands of households. In order to solve the inconvenience problem of the elderly and people with special needs, because the elderly and other people in need may need the help of domestic robots due to inconvenient legs and feet, the research of the robot target position based on monocular stereo vision and the understanding of the robot NAO are very important. Research and experiments are carried out on the target recognition and positioning in the process of NAO robot grasping. This paper proposes a recognition algorithm corresponding to quantitative component statistical information. First, extract the area of interest that contains the purpose from the image. After that, to eliminate interference in various fields and achieve target recognition, the robot cameras have almost no common field of view and can only use one camera at the same time. Therefore, this article uses the monocular vision principle to locate the target, and the detection algorithm is based on the structure of the robot head material, establishes the relationship between the height change of the machine head and the tilt angle, and improves the monocular vision NAO robot detection algorithm. According to experiments, the accuracy of the robot at close range can be controlled below 1 cm. This article completes the robot’s grasping and transmission of the target. About 80% of the external information that humans can perceive comes from vision. In addition, there are advantages such as high efficiency and good stability.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"4 1","pages":"4207694:1-4207694:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76764881","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}
Artificial intelligence is an innovative enterprise theme that combines computer science, physiology, science, and other disciplines, including expert programs, technical experiments, games, native language understanding, cognitive rehabilitation, robotics, engineering, recognition, and other fields. The breakthrough growth made the arrival of the era of big data have a fundamental impact on many traditional industries and has also promoted the transformation of teaching methods and methods in the field of education. In the traditional classroom, due to the limitations of teaching resources, teaching time, and location, students are basically passively accepted by the teacher, and the teaching form is single. This will improve in the age of big data with well-developed network message. Modern distance education uses advanced technology such as computer networks, multimedia, and artificial intelligence to build a virtual teaching environment based on the network. It breaks through the limitations of teachers, teaching materials, experimental equipment, and other resources that exist under the traditional teaching model. Using educational resources of the same scale can expand educational capabilities exponentially and at the same time overcomes the unified progress of traditional teaching methods, single teaching methods, and students’ dissatisfaction. The shortcomings of differences and personalities that are not reflected are conducive to the improvement of the quality of education. It is the original intention of education to offer students special study resources and to stimulate students’ enthusiasm for learning. To enhance the current learning environment, the article builds a personalized multimedia network teaching system based on big data and streaming media technology. By recording the teacher’s class process as a video and uploading it to the multimedia network teaching system, students can watch and download related videos online for learning and also communicate online. The system can also provide personalized and intelligent recommendations for students based on student learning habits. The system is developed based on intelligent multimedia method and has the characteristics of security and stability and has done some help for network teaching of educational institutions.
{"title":"Development and Application of Artificial Intelligence Multimedia Technology Based on Big Data","authors":"Ye Zhang","doi":"10.1155/2022/2073091","DOIUrl":"https://doi.org/10.1155/2022/2073091","url":null,"abstract":"Artificial intelligence is an innovative enterprise theme that combines computer science, physiology, science, and other disciplines, including expert programs, technical experiments, games, native language understanding, cognitive rehabilitation, robotics, engineering, recognition, and other fields. The breakthrough growth made the arrival of the era of big data have a fundamental impact on many traditional industries and has also promoted the transformation of teaching methods and methods in the field of education. In the traditional classroom, due to the limitations of teaching resources, teaching time, and location, students are basically passively accepted by the teacher, and the teaching form is single. This will improve in the age of big data with well-developed network message. Modern distance education uses advanced technology such as computer networks, multimedia, and artificial intelligence to build a virtual teaching environment based on the network. It breaks through the limitations of teachers, teaching materials, experimental equipment, and other resources that exist under the traditional teaching model. Using educational resources of the same scale can expand educational capabilities exponentially and at the same time overcomes the unified progress of traditional teaching methods, single teaching methods, and students’ dissatisfaction. The shortcomings of differences and personalities that are not reflected are conducive to the improvement of the quality of education. It is the original intention of education to offer students special study resources and to stimulate students’ enthusiasm for learning. To enhance the current learning environment, the article builds a personalized multimedia network teaching system based on big data and streaming media technology. By recording the teacher’s class process as a video and uploading it to the multimedia network teaching system, students can watch and download related videos online for learning and also communicate online. The system can also provide personalized and intelligent recommendations for students based on student learning habits. The system is developed based on intelligent multimedia method and has the characteristics of security and stability and has done some help for network teaching of educational institutions.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"40 1","pages":"2073091:1-2073091:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79565946","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}
The traditional mixed oral English teaching model has many obvious shortcomings, such as the inability to correct the students’ oral pronunciation errors and feed them back in time, which leads to the slow improvement of students’ English learning level. For this reason, this paper proposes a guided teaching model based on core literacy. According to the structure of the oral English mixed teaching model, determine the application plan of the oral English mixed teaching model, design the development environment, obtain the corpus, design the oral training model, extract the oral features, identify the wrong pronunciation and correct it in time, clarify the evaluation purpose, obtain preliminary evaluation indicators, reduce evaluation indicators and determine indicator weights, obtain indicator feature information, generate fuzzy rules, obtain fuzzy matrices, achieve quantitative evaluation, and synthesize all evaluation scores to construct a result vector matrix to realize the study of mixed spoken language teaching mode. Research shows that the mixed teaching method is effective and feasible and can effectively improve the accuracy of the evaluation results of the mixed oral English teaching model.
{"title":"Blended Oral English Teaching Based on Core Competence Training Model","authors":"Jiahui Gu","doi":"10.1155/2022/2226544","DOIUrl":"https://doi.org/10.1155/2022/2226544","url":null,"abstract":"The traditional mixed oral English teaching model has many obvious shortcomings, such as the inability to correct the students’ oral pronunciation errors and feed them back in time, which leads to the slow improvement of students’ English learning level. For this reason, this paper proposes a guided teaching model based on core literacy. According to the structure of the oral English mixed teaching model, determine the application plan of the oral English mixed teaching model, design the development environment, obtain the corpus, design the oral training model, extract the oral features, identify the wrong pronunciation and correct it in time, clarify the evaluation purpose, obtain preliminary evaluation indicators, reduce evaluation indicators and determine indicator weights, obtain indicator feature information, generate fuzzy rules, obtain fuzzy matrices, achieve quantitative evaluation, and synthesize all evaluation scores to construct a result vector matrix to realize the study of mixed spoken language teaching mode. Research shows that the mixed teaching method is effective and feasible and can effectively improve the accuracy of the evaluation results of the mixed oral English teaching model.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"124 1","pages":"2226544:1-2226544:9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90385600","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}
Hongyan Wang, Ling Wang, Jiabin Liu, Ying Nie, Daqing Wang
Earthworm manure is a soil enhancement product that is homogeneous, permeable, ecological, and organic. It has a particle structure that is substantially greater than the soil’s surface area. Using a suitable quantity of earthworm fertilizer in the soil will improve the nutritional state of the soil surface, as well as the microbial control system and drainage capacity. Bioengineering earthworm dung is now a tough challenge, but picture quality evaluation can help enhance the organic fertilizer treatment process for earthworm manure. Researchers began researching appropriate assessment methods in order to assure the influence of earthworm excrement and to precisely and effectively measure changes in image quality. As a result, we must first determine the consistency qualities, extract the image's color and texture, and then create a comparable vector with 11 dimensions. Finally, we learn how to train the picture quality regression model using the mechanical learning (ML) approach. As a result, an effective and precise image quality evaluation system was created, and earthworm manure bioengineering was effectively applied.
{"title":"Biological Engineering Analysis of Vermicompost Based on Image Features and Machine Learning","authors":"Hongyan Wang, Ling Wang, Jiabin Liu, Ying Nie, Daqing Wang","doi":"10.1155/2022/7347142","DOIUrl":"https://doi.org/10.1155/2022/7347142","url":null,"abstract":"Earthworm manure is a soil enhancement product that is homogeneous, permeable, ecological, and organic. It has a particle structure that is substantially greater than the soil’s surface area. Using a suitable quantity of earthworm fertilizer in the soil will improve the nutritional state of the soil surface, as well as the microbial control system and drainage capacity. Bioengineering earthworm dung is now a tough challenge, but picture quality evaluation can help enhance the organic fertilizer treatment process for earthworm manure. Researchers began researching appropriate assessment methods in order to assure the influence of earthworm excrement and to precisely and effectively measure changes in image quality. As a result, we must first determine the consistency qualities, extract the image's color and texture, and then create a comparable vector with 11 dimensions. Finally, we learn how to train the picture quality regression model using the mechanical learning (ML) approach. As a result, an effective and precise image quality evaluation system was created, and earthworm manure bioengineering was effectively applied.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"67 1","pages":"7347142:1-7347142:14"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82347931","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 : 2022-01-01DOI: 10.1016/j.is.2021.101867
K. Aberer, I. Katakis, Quoc Viet Hung Nguyen, Hongzhi Yin
{"title":"Preface - Special Issue on Misinformation on the Web","authors":"K. Aberer, I. Katakis, Quoc Viet Hung Nguyen, Hongzhi Yin","doi":"10.1016/j.is.2021.101867","DOIUrl":"https://doi.org/10.1016/j.is.2021.101867","url":null,"abstract":"","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"14 1","pages":"101867"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89464169","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 : 2022-01-01DOI: 10.1016/J.IS.2021.101818
Christina Tsagkani, A. Tsalgatidou
{"title":"Process model abstraction for rapid comprehension of complex business processes","authors":"Christina Tsagkani, A. Tsalgatidou","doi":"10.1016/J.IS.2021.101818","DOIUrl":"https://doi.org/10.1016/J.IS.2021.101818","url":null,"abstract":"","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"37 1","pages":"101818"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73857927","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 : 2022-01-01DOI: 10.1016/J.IS.2021.101848
Maximilian Berens, J. Biskup, M. Preuß
{"title":"Uniform probabilistic generation of relation instances satisfying a functional dependency","authors":"Maximilian Berens, J. Biskup, M. Preuß","doi":"10.1016/J.IS.2021.101848","DOIUrl":"https://doi.org/10.1016/J.IS.2021.101848","url":null,"abstract":"","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"363 1","pages":"101848"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90537945","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 : 2022-01-01DOI: 10.1016/j.is.2021.101872
F. Grandi, F. Mandreoli, R. Martoglia, W. Penzo
{"title":"Unleashing the power of querying streaming data in a temporal database world: A relational algebra approach","authors":"F. Grandi, F. Mandreoli, R. Martoglia, W. Penzo","doi":"10.1016/j.is.2021.101872","DOIUrl":"https://doi.org/10.1016/j.is.2021.101872","url":null,"abstract":"","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"17 1","pages":"101872"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75237192","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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