Stakeholders from academic institutions across the world employ surveys to assess the quality of their work. With surveys these stakeholders attempt to obtain quantified, structured, and directed data in order to make decisions. Often these stakeholders employ long, directed Likert scaled surveys to gain this information. We propose an alternate construction for academic surveys, where stakeholders provide 1-3 open ended "free text" questions, allowing students to lead the discussion. We call this survey methodology "Student Directed Discussion Surveys" (SDDS). SDDS retain the ability to provide quantified, structured, and directed results by employing Natural Language Processing (NLP). We confirm the accuracy of SDDS in relation to traditional Likert scaled surveys with a permutation test, assessing a negligible statistical difference between SDDS and Likert surveys using real data. We then show the utility of SDDS by employing word frequency and sentiment analysis, providing important unbiased decision making information, which is limited when traditional Likert scaled surveys are administered.
{"title":"Improving Student Surveys with Natural Language Processing","authors":"Karoline Hood, Patrick K. Kuiper","doi":"10.1109/IRC.2018.00079","DOIUrl":"https://doi.org/10.1109/IRC.2018.00079","url":null,"abstract":"Stakeholders from academic institutions across the world employ surveys to assess the quality of their work. With surveys these stakeholders attempt to obtain quantified, structured, and directed data in order to make decisions. Often these stakeholders employ long, directed Likert scaled surveys to gain this information. We propose an alternate construction for academic surveys, where stakeholders provide 1-3 open ended \"free text\" questions, allowing students to lead the discussion. We call this survey methodology \"Student Directed Discussion Surveys\" (SDDS). SDDS retain the ability to provide quantified, structured, and directed results by employing Natural Language Processing (NLP). We confirm the accuracy of SDDS in relation to traditional Likert scaled surveys with a permutation test, assessing a negligible statistical difference between SDDS and Likert surveys using real data. We then show the utility of SDDS by employing word frequency and sentiment analysis, providing important unbiased decision making information, which is limited when traditional Likert scaled surveys are administered.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125587215","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}
S. Zanlongo, Franklin Abodo, P. Long, T. Padır, Leonardo Bobadilla
There is a growing need for robots to perform complex tasks autonomously. However, there remain certain tasks that cannot - or should not - be completely automated. While these tasks may require one or several operators, we can oftentimes schedule when an operator should assist. We build on our previous work to present a methodology for allocating operator attention across multiple robots while attempting to minimize the execution time of the robots involved. In this paper, we: 1) Analyze of the complexity of this problem, 2) Provide a scalable methodology for designing robot policies so that few operators can oversee many robots, 3) Describe a methodology for designing both policies and robot trajectories to permit operators to assist many robots, and 4) Present simulation and hardware experiments demonstrating our methodologies.
{"title":"Multi-robot Scheduling and Path-Planning for Non-overlapping Operator Attention","authors":"S. Zanlongo, Franklin Abodo, P. Long, T. Padır, Leonardo Bobadilla","doi":"10.1109/IRC.2018.00021","DOIUrl":"https://doi.org/10.1109/IRC.2018.00021","url":null,"abstract":"There is a growing need for robots to perform complex tasks autonomously. However, there remain certain tasks that cannot - or should not - be completely automated. While these tasks may require one or several operators, we can oftentimes schedule when an operator should assist. We build on our previous work to present a methodology for allocating operator attention across multiple robots while attempting to minimize the execution time of the robots involved. In this paper, we: 1) Analyze of the complexity of this problem, 2) Provide a scalable methodology for designing robot policies so that few operators can oversee many robots, 3) Describe a methodology for designing both policies and robot trajectories to permit operators to assist many robots, and 4) Present simulation and hardware experiments demonstrating our methodologies.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115698892","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}
Efficient storage and querying of long-term human-robot interaction data requires application developers to have an in-depth understanding of the involved domains. It is an error prone task that can immensely impact the interaction experience of humans with robots and artificial agents. In the development cycle of HRI applications, queries towards storage solutions are often created once, copied into according components, and are rarely revisited. Beyond possible syntactical errors (especially impacting query design time), any change in the underlying storage solution structure results in semantic errors at run time which are not easy to spot in existing applications. To address this issue, we present a model-driven software development approach to create a long-term storage system to be used in highly interactive HRI scenarios. We created multiple domain specific languages that allow us to model the domain and seamlessly embed its concepts into a query language. Along with corresponding model-to-model and model-to-text transformations we generate a fully integrated workbench facilitating data storage and retrieval. It supports developers in the query design process and allows in-tool query execution without the need to have prior in-depth knowledge of the domain. We evaluated our work in an extensive user study and can show that the generated tool yields multiple advantages compared to the usual query design approach.
{"title":"A Model Driven Approach for Eased Knowledge Storage and Retrieval in Interactive HRI Systems","authors":"N. Köster, S. Wrede, P. Cimiano","doi":"10.1109/IRC.2018.00025","DOIUrl":"https://doi.org/10.1109/IRC.2018.00025","url":null,"abstract":"Efficient storage and querying of long-term human-robot interaction data requires application developers to have an in-depth understanding of the involved domains. It is an error prone task that can immensely impact the interaction experience of humans with robots and artificial agents. In the development cycle of HRI applications, queries towards storage solutions are often created once, copied into according components, and are rarely revisited. Beyond possible syntactical errors (especially impacting query design time), any change in the underlying storage solution structure results in semantic errors at run time which are not easy to spot in existing applications. To address this issue, we present a model-driven software development approach to create a long-term storage system to be used in highly interactive HRI scenarios. We created multiple domain specific languages that allow us to model the domain and seamlessly embed its concepts into a query language. Along with corresponding model-to-model and model-to-text transformations we generate a fully integrated workbench facilitating data storage and retrieval. It supports developers in the query design process and allows in-tool query execution without the need to have prior in-depth knowledge of the domain. We evaluated our work in an extensive user study and can show that the generated tool yields multiple advantages compared to the usual query design approach.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"2676 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760958","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}
Fog computing has been introduced recently to support the Internet of Things (IoT). It can provide many advantages for IoT applications. These advantages include support for low latency needs, mobility, location awareness, scalability, and efficient integration with other systems such as cloud computing. Fog computing can also provide many advantages for different types of systems like multi-robot systems, which we investigate in this paper. In addition, we study the potential services fog computing can provide for multi-robot system applications and discuss the different issues involved in utilizing fog computing for multi-robot systems.
{"title":"Utilizing Fog Computing for Multi-robot Systems","authors":"N. Mohamed, J. Al-Jaroodi, I. Jawhar","doi":"10.1109/IRC.2018.00023","DOIUrl":"https://doi.org/10.1109/IRC.2018.00023","url":null,"abstract":"Fog computing has been introduced recently to support the Internet of Things (IoT). It can provide many advantages for IoT applications. These advantages include support for low latency needs, mobility, location awareness, scalability, and efficient integration with other systems such as cloud computing. Fog computing can also provide many advantages for different types of systems like multi-robot systems, which we investigate in this paper. In addition, we study the potential services fog computing can provide for multi-robot system applications and discuss the different issues involved in utilizing fog computing for multi-robot systems.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125751287","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}
We introduce a system that exploits a Pepper humanoid robot acting as a playfellow in a word-play game. The robot can play a portmanteau game by directly interacting with children, and it exploits a conversation engine, a portmanteau creation engine, and a definition engine. The humanoid can play the role of either an answerer or a generator of new words.
{"title":"A Social Humanoid Robot as a Playfellow for Vocabulary Enhancement","authors":"Daniele Schicchi, G. Pilato","doi":"10.1109/IRC.2018.00044","DOIUrl":"https://doi.org/10.1109/IRC.2018.00044","url":null,"abstract":"We introduce a system that exploits a Pepper humanoid robot acting as a playfellow in a word-play game. The robot can play a portmanteau game by directly interacting with children, and it exploits a conversation engine, a portmanteau creation engine, and a definition engine. The humanoid can play the role of either an answerer or a generator of new words.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115139653","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：