A model of new workers' accurate acceptance of tasks using capable sensing
Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gong, Dunwei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Development and Initial Validation of the Pain Resilience Scale - Slepian, P. Maxwell ELSEVIER, 2016transfer abstract, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:59 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.swevo.2020.100732 |
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ELV052338061 |
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| 520 | |a Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. | ||
| 520 | |a Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. | ||
| 650 | 7 | |a Crowdsourcing |2 Elsevier | |
| 650 | 7 | |a Genetic algorithm |2 Elsevier | |
| 650 | 7 | |a Accepting task |2 Elsevier | |
| 650 | 7 | |a New worker |2 Elsevier | |
| 650 | 7 | |a Capability aware |2 Elsevier | |
| 700 | 1 | |a Peng, Chao |4 oth | |
| 700 | 1 | |a Yao, Xiangjuan |4 oth | |
| 700 | 1 | |a Tian, Tian |4 oth | |
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10.1016/j.swevo.2020.100732 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001226.pica (DE-627)ELV052338061 (ELSEVIER)S2210-6502(20)30385-0 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Gong, Dunwei verfasserin aut A model of new workers' accurate acceptance of tasks using capable sensing 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing Elsevier Genetic algorithm Elsevier Accepting task Elsevier New worker Elsevier Capability aware Elsevier Peng, Chao oth Yao, Xiangjuan oth Tian, Tian oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:59 year:2020 pages:0 https://doi.org/10.1016/j.swevo.2020.100732 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 59 2020 0 |
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10.1016/j.swevo.2020.100732 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001226.pica (DE-627)ELV052338061 (ELSEVIER)S2210-6502(20)30385-0 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Gong, Dunwei verfasserin aut A model of new workers' accurate acceptance of tasks using capable sensing 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing Elsevier Genetic algorithm Elsevier Accepting task Elsevier New worker Elsevier Capability aware Elsevier Peng, Chao oth Yao, Xiangjuan oth Tian, Tian oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:59 year:2020 pages:0 https://doi.org/10.1016/j.swevo.2020.100732 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 59 2020 0 |
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10.1016/j.swevo.2020.100732 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001226.pica (DE-627)ELV052338061 (ELSEVIER)S2210-6502(20)30385-0 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Gong, Dunwei verfasserin aut A model of new workers' accurate acceptance of tasks using capable sensing 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing Elsevier Genetic algorithm Elsevier Accepting task Elsevier New worker Elsevier Capability aware Elsevier Peng, Chao oth Yao, Xiangjuan oth Tian, Tian oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:59 year:2020 pages:0 https://doi.org/10.1016/j.swevo.2020.100732 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 59 2020 0 |
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10.1016/j.swevo.2020.100732 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001226.pica (DE-627)ELV052338061 (ELSEVIER)S2210-6502(20)30385-0 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Gong, Dunwei verfasserin aut A model of new workers' accurate acceptance of tasks using capable sensing 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing Elsevier Genetic algorithm Elsevier Accepting task Elsevier New worker Elsevier Capability aware Elsevier Peng, Chao oth Yao, Xiangjuan oth Tian, Tian oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:59 year:2020 pages:0 https://doi.org/10.1016/j.swevo.2020.100732 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 59 2020 0 |
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10.1016/j.swevo.2020.100732 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001226.pica (DE-627)ELV052338061 (ELSEVIER)S2210-6502(20)30385-0 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Gong, Dunwei verfasserin aut A model of new workers' accurate acceptance of tasks using capable sensing 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. Crowdsourcing Elsevier Genetic algorithm Elsevier Accepting task Elsevier New worker Elsevier Capability aware Elsevier Peng, Chao oth Yao, Xiangjuan oth Tian, Tian oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:59 year:2020 pages:0 https://doi.org/10.1016/j.swevo.2020.100732 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 59 2020 0 |
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Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. |
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Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. |
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Crowdsourcing has been one of focuses in academic and industrial communities along with rapid development and wide-spread applications of Internet. However, the lack of a new worker's capacity of accepting tasks seriously affects his/her income obtained by fulfilling tasks issued by requesters, which reduces his/her enthusiasm for participation in crowdsourcing. We propose a method of solving the problem of accurately accepting tasks for a new worker in this paper. To fulfill this task, we firstly formulate the problem as a constraint optimization problem with an unknown parameter which shows the time consumption in fulfilling a task by a new worker. Then, we estimate the time consumption using information about similar tasks and workers in the crowdsourcing platform. Finally, we generate a strategy of accepting tasks by solving the optimization problem using a genetic algorithm, with the purpose of maximizing the income of the new work. We evaluate the effectiveness of the proposed strategy based on data in Taskcn, a representative commercial crowdsourcing platform in China, by comparing the results with a number of workers' actual earning. The experimental results demonstrate the accuracy of the new worker's capacity of accepting tasks, which is beneficial for generating a strategy to improve his/her income. |
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