Pub Date : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00027
Christine Bassem
In participatory Mobile CrowdSensing, tasks are allocated to participants via some allocation mechanism, which are challenging in terms of their evaluation due to the lack of general-purpose, modular, and extendable simulators. Thus, forcing researchers to either launch their own testbeds or develop single-purpose simulators.In this paper, we present our design and implementation of an extendable simulator, namely TACSim, for the evaluation of task allocation mechanisms in a participatory MCS setting over realistic urban environments. TACSim is designed to accommodate realistic urban road networks, as well as spatio-temporal traces of sensing tasks and participant mobility. It includes built-in autonomous task allocation mechanisms, and can be extended by researchers to accommodate their own algorithms with minimal effort. We discuss the components and architecture of the simulator, and present a use-case of integrating existing autonomous task allocation mechanisms that further exemplifies the usability and extendability of the simulator.
{"title":"TACSim: An Extendable Simulator for Task Allocation Mechanisms in CrowdSensing","authors":"Christine Bassem","doi":"10.1109/SMARTCOMP58114.2023.00027","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00027","url":null,"abstract":"In participatory Mobile CrowdSensing, tasks are allocated to participants via some allocation mechanism, which are challenging in terms of their evaluation due to the lack of general-purpose, modular, and extendable simulators. Thus, forcing researchers to either launch their own testbeds or develop single-purpose simulators.In this paper, we present our design and implementation of an extendable simulator, namely TACSim, for the evaluation of task allocation mechanisms in a participatory MCS setting over realistic urban environments. TACSim is designed to accommodate realistic urban road networks, as well as spatio-temporal traces of sensing tasks and participant mobility. It includes built-in autonomous task allocation mechanisms, and can be extended by researchers to accommodate their own algorithms with minimal effort. We discuss the components and architecture of the simulator, and present a use-case of integrating existing autonomous task allocation mechanisms that further exemplifies the usability and extendability of the simulator.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693896","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00054
Jingyi Li, Qi Chen, Wen Wang, Fangyu Wu
Social media plays an irreplaceable role in shaping the way information is created shared and consumed. While it provides access to a vast amount of data, extracting and analyzing useful insights from complex and dynamic social media data can be challenging. Deep learning models have shown promise in social media analysis tasks, but such models require a massive amount of labelled data which is usually unavailable in real-world settings. Additionally, these models lack common-sense knowledge which can limit their ability to generate comprehensive results. To address these challenges, we propose a knowledge-embedded prompt learning model for zero-shot social media text classification. Our experimental results on four social media datasets demonstrate that our proposed approach outperforms other well-known baselines.
{"title":"Knowledge-embedded Prompt Learning for Zero-shot Social Media Text Classification","authors":"Jingyi Li, Qi Chen, Wen Wang, Fangyu Wu","doi":"10.1109/SMARTCOMP58114.2023.00054","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00054","url":null,"abstract":"Social media plays an irreplaceable role in shaping the way information is created shared and consumed. While it provides access to a vast amount of data, extracting and analyzing useful insights from complex and dynamic social media data can be challenging. Deep learning models have shown promise in social media analysis tasks, but such models require a massive amount of labelled data which is usually unavailable in real-world settings. Additionally, these models lack common-sense knowledge which can limit their ability to generate comprehensive results. To address these challenges, we propose a knowledge-embedded prompt learning model for zero-shot social media text classification. Our experimental results on four social media datasets demonstrate that our proposed approach outperforms other well-known baselines.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127520150","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00022
Riccardo Presotto, Sannara Ek, Gabriele Civitarese, François Portet, P. Lalanda, C. Bettini
The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts of labeled data, both for the initial training of the models and for their customization on specific clients (whose data often differ greatly from the training data). This is actually impractical to obtain due to the costs, intrusiveness, and time-consuming nature of data annotation. Moreover, even with the help of a significant amount of labeled data, model deployment on heterogeneous clients faces difficulties in generalizing well on unseen data. Other domains, like Computer Vision or Natural Language Processing, have proposed the notion of pre-trained models, leveraging large corpora, to reduce the need for annotated data and better manage heterogeneity. This promising approach has not been implemented in the HAR domain so far because of the lack of public datasets of sufficient size. In this paper, we propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model that can be fine-tuned using a limited amount of labeled data on an unseen target domain. Our experimental evaluation, which includes experimenting with different state-of-the-art neural network architectures, shows that combining public datasets can significantly reduce the number of labeled samples required to achieve satisfactory performance on an unseen target domain.
{"title":"Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity","authors":"Riccardo Presotto, Sannara Ek, Gabriele Civitarese, François Portet, P. Lalanda, C. Bettini","doi":"10.1109/SMARTCOMP58114.2023.00022","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00022","url":null,"abstract":"The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts of labeled data, both for the initial training of the models and for their customization on specific clients (whose data often differ greatly from the training data). This is actually impractical to obtain due to the costs, intrusiveness, and time-consuming nature of data annotation. Moreover, even with the help of a significant amount of labeled data, model deployment on heterogeneous clients faces difficulties in generalizing well on unseen data. Other domains, like Computer Vision or Natural Language Processing, have proposed the notion of pre-trained models, leveraging large corpora, to reduce the need for annotated data and better manage heterogeneity. This promising approach has not been implemented in the HAR domain so far because of the lack of public datasets of sufficient size. In this paper, we propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model that can be fine-tuned using a limited amount of labeled data on an unseen target domain. Our experimental evaluation, which includes experimenting with different state-of-the-art neural network architectures, shows that combining public datasets can significantly reduce the number of labeled samples required to achieve satisfactory performance on an unseen target domain.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133384854","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00061
Debjyoti Sengupta
Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles is envisioned as an emerging disruptive technology for next generation smart transportation that addresses mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-time autonomous scheduling, dynamic route planning, aerial-to-ground and inter-vehicle communications, airspace traffic management, on-demand air mobility, resource management, quality of service and quality of experience, sensing (edge) analytics and machine learning for trustworthy decision making, optimization of operational services, and socio-economic impacts of UAM infrastructure on sustainability.
{"title":"Investigating Computational Aspects and Potential Challenges in Implementing Urban Air Mobility","authors":"Debjyoti Sengupta","doi":"10.1109/SMARTCOMP58114.2023.00061","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00061","url":null,"abstract":"Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles is envisioned as an emerging disruptive technology for next generation smart transportation that addresses mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-time autonomous scheduling, dynamic route planning, aerial-to-ground and inter-vehicle communications, airspace traffic management, on-demand air mobility, resource management, quality of service and quality of experience, sensing (edge) analytics and machine learning for trustworthy decision making, optimization of operational services, and socio-economic impacts of UAM infrastructure on sustainability.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130520884","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00063
Nathan Puryear
This paper presents work in progress towards a system modeling and co-emulation framework for distributed cyber-physical system (CPS) environments. The proposed framework aims to support experiential learning and experiment orchestration in environments such as CPS testbeds and chemistry labs. It addresses challenges of interoperability, multi-tenancy, scalability and security by leveraging a novel "co-emulation" approach that combines different modeling, orchestration and runtime tools.
{"title":"System Modeling and Co-Emulation for Distributed Cyber-Physical System Environments","authors":"Nathan Puryear","doi":"10.1109/SMARTCOMP58114.2023.00063","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00063","url":null,"abstract":"This paper presents work in progress towards a system modeling and co-emulation framework for distributed cyber-physical system (CPS) environments. The proposed framework aims to support experiential learning and experiment orchestration in environments such as CPS testbeds and chemistry labs. It addresses challenges of interoperability, multi-tenancy, scalability and security by leveraging a novel \"co-emulation\" approach that combines different modeling, orchestration and runtime tools.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122620876","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00049
Hamada Rizk, Hirozumi Yamaguchi
In this paper, we propose KissLoc: a system that leverages onboard micro-size sensors of consumer eyewear devices for the dual purpose of activity recognition and localization. Specifically, the system trains a deep learning model for recognizing kissing activity and simultaneously identifying the timestamped location of its occurrence. Consequently, several predefined actions could be taken, including logging or controlling the smart environment. The evaluation shows that KissLoc can recognize the kissing activity with 82% accuracy while locating its occurrence with a median localization error of 1.25m.
{"title":"KissLoc: A Spatio-temporal Kissing Recognition System Using Commercial Smart Glasses","authors":"Hamada Rizk, Hirozumi Yamaguchi","doi":"10.1109/SMARTCOMP58114.2023.00049","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00049","url":null,"abstract":"In this paper, we propose KissLoc: a system that leverages onboard micro-size sensors of consumer eyewear devices for the dual purpose of activity recognition and localization. Specifically, the system trains a deep learning model for recognizing kissing activity and simultaneously identifying the timestamped location of its occurrence. Consequently, several predefined actions could be taken, including logging or controlling the smart environment. The evaluation shows that KissLoc can recognize the kissing activity with 82% accuracy while locating its occurrence with a median localization error of 1.25m.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130047924","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00044
M. Hossain
In this paper, we focus on improving the age estimation accuracy on smartphones. Estimating a smartphone user’s age has several applications such as protecting our children online by filtering age-inappropriate contents, providing a customized e-commerce experience, etc. However, accuracy of the the state-of-the-art age estimation techniques that use touch behavior on smartphones is still limited because of the lack of sufficient amount of training data. We perform rigorous experiments using zoom gestures on smartphones and demonstrate that increasing the amount of training data can significantly improve the age estimation accuracy. Based on the findings in this study, we recommend creating a large touch dynamics-based age estimation data set so that more accurate age estimation models can be built and in turn, can be used more confidently.
{"title":"A Case Study Using Zoom Touch Gestures: How Does the Size of a Training Dataset Impact User’s Age Estimation Accuracy in Smartphones?","authors":"M. Hossain","doi":"10.1109/SMARTCOMP58114.2023.00044","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00044","url":null,"abstract":"In this paper, we focus on improving the age estimation accuracy on smartphones. Estimating a smartphone user’s age has several applications such as protecting our children online by filtering age-inappropriate contents, providing a customized e-commerce experience, etc. However, accuracy of the the state-of-the-art age estimation techniques that use touch behavior on smartphones is still limited because of the lack of sufficient amount of training data. We perform rigorous experiments using zoom gestures on smartphones and demonstrate that increasing the amount of training data can significantly improve the age estimation accuracy. Based on the findings in this study, we recommend creating a large touch dynamics-based age estimation data set so that more accurate age estimation models can be built and in turn, can be used more confidently.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116386571","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00045
M. Soderi, J. Breslin
In modern industry, adaptation to market changes, as well as prompt reaction to a variety of predictable and unpredictable events, is a key requirement. Ubiquitous computing, real-time analytics, reconfigurable hardware/software components, often coexist in the complex, internally variegated, and often proprietary systems that are traditionally deployed to meet such requirement. However, such tailor-made systems meet only in part the requirements of openness, security, monitorability, geographical distribution, and most of all, remote extendability and changeability, which are crucial for prompt reaction to unforeseen circumstances. In this work, a containerized service application named Network Factory is presented. It enables the remote construction, configuration and operation of resilient computation systems that meet the above-mentioned requirements, and distinguish for their logical simplicity and for the uniform addressing of elaborations and human-computer interfaces, which are achieved through few reconfigurable components and communication mechanisms that are used from the production line up to the Cloud. Source code, documentation, and step-by-step introductory guides are publicly available in a dedicated GitHub repository, and distributed under the CC-BY-4.0 license.
{"title":"A Service for Resilient Manufacturing","authors":"M. Soderi, J. Breslin","doi":"10.1109/SMARTCOMP58114.2023.00045","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00045","url":null,"abstract":"In modern industry, adaptation to market changes, as well as prompt reaction to a variety of predictable and unpredictable events, is a key requirement. Ubiquitous computing, real-time analytics, reconfigurable hardware/software components, often coexist in the complex, internally variegated, and often proprietary systems that are traditionally deployed to meet such requirement. However, such tailor-made systems meet only in part the requirements of openness, security, monitorability, geographical distribution, and most of all, remote extendability and changeability, which are crucial for prompt reaction to unforeseen circumstances. In this work, a containerized service application named Network Factory is presented. It enables the remote construction, configuration and operation of resilient computation systems that meet the above-mentioned requirements, and distinguish for their logical simplicity and for the uniform addressing of elaborations and human-computer interfaces, which are achieved through few reconfigurable components and communication mechanisms that are used from the production line up to the Cloud. Source code, documentation, and step-by-step introductory guides are publicly available in a dedicated GitHub repository, and distributed under the CC-BY-4.0 license.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126741311","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00077
M. Casillo, Liliana Cecere, F. Colace, Angelo Lorusso, Francesco Marongiu, D. Santaniello
Spa treatments may mistakenly be considered palliative compared to traditional medicines; however, this is not the case. Mineral/thermal waters are medicines for all intents and purposes and should be analyzed and used as such. The difference in spa treatments compared to other medicines is the greater complexity with which they are delivered. Patients must follow a course of treatment that can last up to a couple of weeks, during which the effects of the therapy gradually go into evidence. Both inside and outside the spa facility, having patient monitoring could be a valuable tool to measure the effectiveness of treatment and possibly even intervene with personalized care based on the parameters detected. New technologies and paradigms such as the Internet of Things can offer a valuable tool to improve spa care through active monitoring of patients, both inside and outside the facilities, by going to measure what are the key parameters (i.e., heart rate, blood oxygenation, etc.) to track the progress of the therapy accurately and precisely during treatment. In particular, wearable devices (smartwatches or smart bands) can perform constant and non-invasive monitoring of the patient's status and the therapy itself. Therefore, the work aims to define a framework based on the Internet of Things paradigm for intelligent analysis of spa treatments to manage patients correctly.
{"title":"Internet of Things in SPA Medicine: A General Framework to Improve User Treatments","authors":"M. Casillo, Liliana Cecere, F. Colace, Angelo Lorusso, Francesco Marongiu, D. Santaniello","doi":"10.1109/SMARTCOMP58114.2023.00077","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00077","url":null,"abstract":"Spa treatments may mistakenly be considered palliative compared to traditional medicines; however, this is not the case. Mineral/thermal waters are medicines for all intents and purposes and should be analyzed and used as such. The difference in spa treatments compared to other medicines is the greater complexity with which they are delivered. Patients must follow a course of treatment that can last up to a couple of weeks, during which the effects of the therapy gradually go into evidence. Both inside and outside the spa facility, having patient monitoring could be a valuable tool to measure the effectiveness of treatment and possibly even intervene with personalized care based on the parameters detected. New technologies and paradigms such as the Internet of Things can offer a valuable tool to improve spa care through active monitoring of patients, both inside and outside the facilities, by going to measure what are the key parameters (i.e., heart rate, blood oxygenation, etc.) to track the progress of the therapy accurately and precisely during treatment. In particular, wearable devices (smartwatches or smart bands) can perform constant and non-invasive monitoring of the patient's status and the therapy itself. Therefore, the work aims to define a framework based on the Internet of Things paradigm for intelligent analysis of spa treatments to manage patients correctly.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805794","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 : 2023-06-01DOI: 10.1109/SMARTCOMP58114.2023.00083
J. Rodano, Omar Obidat, Jesse Parron, Rui Li, Michelle Zhu, Weitian Wang
As technology has advanced, society has witnessed and participated in the creation of robots that can walk, talk, and recognize speech. To facilitate communication and collaboration between humans and humanoid robots, we develop a teaching-learning framework for human beings to teach humanoid robots to complete object identification and operation tasks. The robots learn from their human partners based on the transfer learning approach and can assist humans using their learned knowledge. Experimental results and evaluations suggest the success and efficiency of the developed approach in smart service contexts for human-robot partnerships. The future work of this study is also discussed.
{"title":"Teaching Humanoid Robots to Assist Humans for Collaborative Tasks","authors":"J. Rodano, Omar Obidat, Jesse Parron, Rui Li, Michelle Zhu, Weitian Wang","doi":"10.1109/SMARTCOMP58114.2023.00083","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00083","url":null,"abstract":"As technology has advanced, society has witnessed and participated in the creation of robots that can walk, talk, and recognize speech. To facilitate communication and collaboration between humans and humanoid robots, we develop a teaching-learning framework for human beings to teach humanoid robots to complete object identification and operation tasks. The robots learn from their human partners based on the transfer learning approach and can assist humans using their learned knowledge. Experimental results and evaluations suggest the success and efficiency of the developed approach in smart service contexts for human-robot partnerships. The future work of this study is also discussed.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330606","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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