Pub Date : 2022-09-26DOI: 10.1109/ISC255366.2022.9922427
Ebaa Alnazer, Ilche Georgievski, Neha Prakash, Marco Aiello
The adoption of autonomous vehicles mainly depends on the driver's trust in the vehicle's capabilities. Influencing trust requires giving it a central role when designing the vehicle's functionalities, including the one for driving from one location to another. Addressing this driving task requires not only considering environmental and vehicle's conditions (e.g., road obstacles, fuel level, but also factors that influence trust, such as variability of trust, use of understandable and structured knowledge, and operation transparency. One way to address such a driving task is to solve it as a planning problem. Among AI planning techniques, Hierarchical Task Network (HTN) planning provides a powerful approach to model rich domain knowledge using hierarchical constructs, simulating the way in which one conceptualises knowledge and performs decision making. Here, we analyse the suitability of HTN planning for the trust-based driving task and define the respective planning problem. Based on this, we model an HTN domain for the driving task and use it to solve the driving task in two case studies. The results indicate that trust-based HTN planning provides a feasible approach for efficiently computing plans that maximise trust.
{"title":"A Role for HTN Planning in Increasing Trust in Autonomous Driving","authors":"Ebaa Alnazer, Ilche Georgievski, Neha Prakash, Marco Aiello","doi":"10.1109/ISC255366.2022.9922427","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922427","url":null,"abstract":"The adoption of autonomous vehicles mainly depends on the driver's trust in the vehicle's capabilities. Influencing trust requires giving it a central role when designing the vehicle's functionalities, including the one for driving from one location to another. Addressing this driving task requires not only considering environmental and vehicle's conditions (e.g., road obstacles, fuel level, but also factors that influence trust, such as variability of trust, use of understandable and structured knowledge, and operation transparency. One way to address such a driving task is to solve it as a planning problem. Among AI planning techniques, Hierarchical Task Network (HTN) planning provides a powerful approach to model rich domain knowledge using hierarchical constructs, simulating the way in which one conceptualises knowledge and performs decision making. Here, we analyse the suitability of HTN planning for the trust-based driving task and define the respective planning problem. Based on this, we model an HTN domain for the driving task and use it to solve the driving task in two case studies. The results indicate that trust-based HTN planning provides a feasible approach for efficiently computing plans that maximise trust.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133687611","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922444
Jerahmeel K. Coching, Adrian Jenssen L. Pe, Seth Gabriel D. Yeung, W. Akeboshi, R. Billones
Manufacturing firms are constantly faced with operational challenges of ensuring that their existing manufacturing processes and systems can deliver expected production rates of high-quality products while minimizing the extensive use of resources. This holds for the Sakthi Auto Component Ltd. (SACL) core shop based in Coimbatore, TN, India, whose current core shop manufacturing production infrastructure consists of eleven distinct machines responsible for carrying out an eleven-step manufacturing process. Several case studies have been conducted to research possible interventions to improve the system. Despite adopting machining process modifications, system evaluations show that the manufacturing production line is still considered lacking and unable to meet the average 24300 monthly core demand. Bottleneck analysis is a standard manufacturing and production management approach to evaluate and increase system capacities relative to utilization and efficiency metrics. This paper illustrates an opportunity presented by Cyber-Physical systems (CPS) to automate the validation of improvements that are based on iterative implementations of bottleneck analysis by modelling the manufacturing production infrastructure of SACL using MATLAB SIMULINK. Following successive iterations of bottleneck analysis, the final model configuration could meet the expected demand of 24300 cores within 27 working days, with each day having a total of 8 work hours, and only 7 hours being productive. The simulation reported an 87.47% system utilization rate and a 99.96% system efficiency rate.
{"title":"Cyber-Physical System Modeling for Bottleneck Analysis of the Manufacturing Production Line of Core Machines","authors":"Jerahmeel K. Coching, Adrian Jenssen L. Pe, Seth Gabriel D. Yeung, W. Akeboshi, R. Billones","doi":"10.1109/ISC255366.2022.9922444","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922444","url":null,"abstract":"Manufacturing firms are constantly faced with operational challenges of ensuring that their existing manufacturing processes and systems can deliver expected production rates of high-quality products while minimizing the extensive use of resources. This holds for the Sakthi Auto Component Ltd. (SACL) core shop based in Coimbatore, TN, India, whose current core shop manufacturing production infrastructure consists of eleven distinct machines responsible for carrying out an eleven-step manufacturing process. Several case studies have been conducted to research possible interventions to improve the system. Despite adopting machining process modifications, system evaluations show that the manufacturing production line is still considered lacking and unable to meet the average 24300 monthly core demand. Bottleneck analysis is a standard manufacturing and production management approach to evaluate and increase system capacities relative to utilization and efficiency metrics. This paper illustrates an opportunity presented by Cyber-Physical systems (CPS) to automate the validation of improvements that are based on iterative implementations of bottleneck analysis by modelling the manufacturing production infrastructure of SACL using MATLAB SIMULINK. Following successive iterations of bottleneck analysis, the final model configuration could meet the expected demand of 24300 cores within 27 working days, with each day having a total of 8 work hours, and only 7 hours being productive. The simulation reported an 87.47% system utilization rate and a 99.96% system efficiency rate.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130080787","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922157
Georgios Tziolis, Anastasios Koumis, S. Theocharides, Andreas Livera, Javier Lopez-Lorente, G. Makrides, G. Georghiou
Net load forecasting is essential for the reliable, safe and cost-effective operation of modern power systems incorporating variable renewable technologies. This paper proposes a short-term net load forecasting (STNLF) methodology based on Bayesian neural networks, applicable to microgrids with embedded photovoltaic (PV) systems. Input feature selection and determination of hidden nodes were performed to develop an optimally performing forecasting model. To validate the performance of the model, historical net load site-specific and aggregated data from buildings within the University of Cyprus microgrid (with integrated PV shares of 26%) were used. The developed STNLF model demonstrated a normalized root mean square error of 4.81 % and 3.98% for the solar-integrated building and the microgrid, respectively. Finally, the capability of the developed machine learning forecasting model to yield reliable forecasts was benchmarked against baseline naïve persistence forecasts, achieving skill score improvements of up to 18.61 % at microgrid level.
{"title":"Advanced Short-Term Net Load Forecasting for Renewable-Based Microgrids","authors":"Georgios Tziolis, Anastasios Koumis, S. Theocharides, Andreas Livera, Javier Lopez-Lorente, G. Makrides, G. Georghiou","doi":"10.1109/ISC255366.2022.9922157","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922157","url":null,"abstract":"Net load forecasting is essential for the reliable, safe and cost-effective operation of modern power systems incorporating variable renewable technologies. This paper proposes a short-term net load forecasting (STNLF) methodology based on Bayesian neural networks, applicable to microgrids with embedded photovoltaic (PV) systems. Input feature selection and determination of hidden nodes were performed to develop an optimally performing forecasting model. To validate the performance of the model, historical net load site-specific and aggregated data from buildings within the University of Cyprus microgrid (with integrated PV shares of 26%) were used. The developed STNLF model demonstrated a normalized root mean square error of 4.81 % and 3.98% for the solar-integrated building and the microgrid, respectively. Finally, the capability of the developed machine learning forecasting model to yield reliable forecasts was benchmarked against baseline naïve persistence forecasts, achieving skill score improvements of up to 18.61 % at microgrid level.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114331910","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922474
Jennifer Williams, Benjamin Lellouch, S. Stein, C. Vanderwel, S. Gauthier
We present critical research challenges for the development of smart building management systems (BMS) to achieve low-carbon comfort. To date, work in this area has focused on optimising single-scope aspects of building resources, such as energy usage or thermal comfort, but there is a recent shift toward BMS design that could simultaneously address many aspects of building resources and comfort dimensions for occupants, such as air quality, temperature, humidity, audible noise levels, and related automated safety features. In this paper, we discuss four research directions highlighting current challenges in this domain that present opportunities for research: (A) data limitations for machine learning, (B) multiple definitions of comfort, (C) BMS usability and interfaces, and (D) safety and security of automated BMS decision-making. Addressing these challenges will enable the development of advanced human-centred energy-saving buildings that meet the needs of occupants.
{"title":"Low-Carbon Comfort Management for Smart Buildings","authors":"Jennifer Williams, Benjamin Lellouch, S. Stein, C. Vanderwel, S. Gauthier","doi":"10.1109/ISC255366.2022.9922474","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922474","url":null,"abstract":"We present critical research challenges for the development of smart building management systems (BMS) to achieve low-carbon comfort. To date, work in this area has focused on optimising single-scope aspects of building resources, such as energy usage or thermal comfort, but there is a recent shift toward BMS design that could simultaneously address many aspects of building resources and comfort dimensions for occupants, such as air quality, temperature, humidity, audible noise levels, and related automated safety features. In this paper, we discuss four research directions highlighting current challenges in this domain that present opportunities for research: (A) data limitations for machine learning, (B) multiple definitions of comfort, (C) BMS usability and interfaces, and (D) safety and security of automated BMS decision-making. Addressing these challenges will enable the development of advanced human-centred energy-saving buildings that meet the needs of occupants.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133754037","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922373
Pedro Núñez-Cacho, Juan Manuel Maqueira-Marín, B. Minguela-Rata, V. Molina-Moreno
A smart city that implements the principles of the Circular Economy (CE) is called a Circular and Smart city. One of the dimensions that make up a CE, one with great weight and importance, is the management of emissions, which in the specific case of cities affects air quality and health of citizens. This work proposes a smart management model for air quality in smart circular cities, in which the various factors that condition it are integrated. In this intelligent management model, data is collected and the application of Artificial Intelligence algorithms is proposed on these data. They not only perform an analysis of the current state of the air, but also allow predictive information to be obtained on how air quality will evolve in the future, an aspect that would allow those responsible for sustainable circular cities to make appropriate decisions.
{"title":"Building a model for the predictive improvement of air quality in Circular Smart cities","authors":"Pedro Núñez-Cacho, Juan Manuel Maqueira-Marín, B. Minguela-Rata, V. Molina-Moreno","doi":"10.1109/ISC255366.2022.9922373","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922373","url":null,"abstract":"A smart city that implements the principles of the Circular Economy (CE) is called a Circular and Smart city. One of the dimensions that make up a CE, one with great weight and importance, is the management of emissions, which in the specific case of cities affects air quality and health of citizens. This work proposes a smart management model for air quality in smart circular cities, in which the various factors that condition it are integrated. In this intelligent management model, data is collected and the application of Artificial Intelligence algorithms is proposed on these data. They not only perform an analysis of the current state of the air, but also allow predictive information to be obtained on how air quality will evolve in the future, an aspect that would allow those responsible for sustainable circular cities to make appropriate decisions.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121843264","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921979
P. Fussey, J. Dalby
Mobility solutions are increasingly aware of their location and can be considered as elements within a connected transport system. In this context, geofencing refers to the use of a vehicle position to change its behaviour to improve the overall environment for the whole transport system. In this paper, we start with advances in simulation tools for optimizing the use of geofences for both existing and new vehicles, covering both vehicle performance and the overall impact on air quality. The results of the simulations have been demonstrated in a connected city environment resulting in significant reductions in emissions and energy consumption for two applications; a geofence enabled bus and a fleet of existing hybrid electric vehicles using a smartphone app to enable geofence zones.
{"title":"Optimisation of geofencing for mobility solutions in smart cities","authors":"P. Fussey, J. Dalby","doi":"10.1109/ISC255366.2022.9921979","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921979","url":null,"abstract":"Mobility solutions are increasingly aware of their location and can be considered as elements within a connected transport system. In this context, geofencing refers to the use of a vehicle position to change its behaviour to improve the overall environment for the whole transport system. In this paper, we start with advances in simulation tools for optimizing the use of geofences for both existing and new vehicles, covering both vehicle performance and the overall impact on air quality. The results of the simulations have been demonstrated in a connected city environment resulting in significant reductions in emissions and energy consumption for two applications; a geofence enabled bus and a fleet of existing hybrid electric vehicles using a smartphone app to enable geofence zones.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"27 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281123","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922123
Ashley S. Bangert, G. Nunez-Mchiri, O. Mondragon, Daniel Calvo, Christian Ruiz, Edgar J. Escobedo, N. Villanueva-Rosales, R. Cheu
Older adults may struggle to maintain social connections as their lifestyles change, e.g., retirement. Their social connectedness can be fostered by using technology; however, many factors hinder older adults' technology use, creating a generational digital divide. This paper presents a study to learn about best practices for teaching and engaging older adults using technology. This study was done in the Summer and Fall of 2021 (during the COVID-19 pandemic) and consisted of an online survey, course observations, and focus groups with instructors. We found that a) older adults were often intrinsically motivated to take online courses; b) not having access to appropriate technology tools or infrastructure, and not having the knowledge or confidence needed to utilize technology created barriers that hinder older adults' participation in online courses; c) the instructors modified their courses to incorporate time to socialize, make classes more interactive, and refocus on the learning process rather than the outcomes; d) capitalizing on older adults' strengths and encouraging reciprocity was crucial for online learning; and e) there is a need to train instructors and students on the use of technology to teach and to learn online. This study's findings contribute to understanding how older adults learn with technology. Through technology engagement that facilitates learning, older adults may improve their quality of life and become empowered as critical agents in Smart Cities.
{"title":"Using Technology to Teach Older Adults during the COVID-19 pandemic","authors":"Ashley S. Bangert, G. Nunez-Mchiri, O. Mondragon, Daniel Calvo, Christian Ruiz, Edgar J. Escobedo, N. Villanueva-Rosales, R. Cheu","doi":"10.1109/ISC255366.2022.9922123","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922123","url":null,"abstract":"Older adults may struggle to maintain social connections as their lifestyles change, e.g., retirement. Their social connectedness can be fostered by using technology; however, many factors hinder older adults' technology use, creating a generational digital divide. This paper presents a study to learn about best practices for teaching and engaging older adults using technology. This study was done in the Summer and Fall of 2021 (during the COVID-19 pandemic) and consisted of an online survey, course observations, and focus groups with instructors. We found that a) older adults were often intrinsically motivated to take online courses; b) not having access to appropriate technology tools or infrastructure, and not having the knowledge or confidence needed to utilize technology created barriers that hinder older adults' participation in online courses; c) the instructors modified their courses to incorporate time to socialize, make classes more interactive, and refocus on the learning process rather than the outcomes; d) capitalizing on older adults' strengths and encouraging reciprocity was crucial for online learning; and e) there is a need to train instructors and students on the use of technology to teach and to learn online. This study's findings contribute to understanding how older adults learn with technology. Through technology engagement that facilitates learning, older adults may improve their quality of life and become empowered as critical agents in Smart Cities.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124351894","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922368
Elisavet Grigoriou, Achilleas Moukoulis, Theocharis Saoulidis, Rita Santiago, Helio Simeão, Sonia Castro, Paula Encinar Sanz, Inmaculada Prieto Borrero, A. Betzler, Sergi Cadenas, I. Ciornei
Smart5Grid exploits the robustness and flexibility enabled by the 5-th generation (5G) mobile network technology to contribute an open and adaptable platform for reliable testing, validation, and operation of Network Applications (NetApps), addressing the challenges of the Renewable Energy Sources (RES) production and those of the operation of the active power distribution ecosystem, in particular. Smart5Grid aims to support current energy sector and future smart grid stakeholders through the adaptation of 5G networks and the support of the respective NetApps that will be developed and validated on real power grid facilities. Smart5Grid intends to provide a more secure, flexible, efficient, scalable and real-time communication framework for modern smart grids. Through remote inspection and control of automatically delimited working areas, the capabilities of the created Smart5Grid 5G platform will facilitate the improvement of working conditions for power grid maintenance crews and inspection workers. In this paper, we presented the methodology of the Smart5Grid project, which will enable remote inspections in high-risk areas and real-time execution by supporting various distribution network applications and providing accurate results and information on the operational condition of power grid assets via augmented reality. If maintenance work is undertaken, real-time control will be enabled to support working procedures remotely and automatically, without putting employees in potentially harmful conditions.
{"title":"Remote Monitoring at Distribution Network of Dynamically Constrained Working Areas","authors":"Elisavet Grigoriou, Achilleas Moukoulis, Theocharis Saoulidis, Rita Santiago, Helio Simeão, Sonia Castro, Paula Encinar Sanz, Inmaculada Prieto Borrero, A. Betzler, Sergi Cadenas, I. Ciornei","doi":"10.1109/ISC255366.2022.9922368","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922368","url":null,"abstract":"Smart5Grid exploits the robustness and flexibility enabled by the 5-th generation (5G) mobile network technology to contribute an open and adaptable platform for reliable testing, validation, and operation of Network Applications (NetApps), addressing the challenges of the Renewable Energy Sources (RES) production and those of the operation of the active power distribution ecosystem, in particular. Smart5Grid aims to support current energy sector and future smart grid stakeholders through the adaptation of 5G networks and the support of the respective NetApps that will be developed and validated on real power grid facilities. Smart5Grid intends to provide a more secure, flexible, efficient, scalable and real-time communication framework for modern smart grids. Through remote inspection and control of automatically delimited working areas, the capabilities of the created Smart5Grid 5G platform will facilitate the improvement of working conditions for power grid maintenance crews and inspection workers. In this paper, we presented the methodology of the Smart5Grid project, which will enable remote inspections in high-risk areas and real-time execution by supporting various distribution network applications and providing accurate results and information on the operational condition of power grid assets via augmented reality. If maintenance work is undertaken, real-time control will be enabled to support working procedures remotely and automatically, without putting employees in potentially harmful conditions.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122779541","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921873
Katerina Tzafilkou, Dimitrios Karapiperis, Vassilios S. Verykios
Mouse tracking can be used as a non-obtrusive data-collection method to identify in real time the users' cognitive and emotional states. Despite the advances in the field, most studies focus on measuring decision conflict processes in typical choice-making tasks, while a framework for emotion prediction in different contexts of web interactions is missing. The present study investigates the potential of measuring a person's negative emotional state through solely mouse cursor data of speed and acceleration. A two study experiment was designed to monitor the mouse behavior of 79 participants in three different types of gaming apps: two gamified campaigns (a puzzle and a hidden-items game), and one Game-based Learning (GBL) quiz task. The collected dataset comprised 123 valid records of mouse features and self-reported emotional statements. A set of different classifiers were trained and tested, where we achieved a maximum accuracy of 81% and 83% for frustration and confusion, respectively. We also achieved higher accuracy, namely 85%, in the case of gamified tasks, excluding the GBL task, implying that further research should be conducted in this field. Our findings indicate that by analyzing speed and acceleration data, it is possible to make efficient predictions of a user's emotional state in different web activities.
{"title":"Empowering Affect-Aware Systems by Monitoring Mouse Speed and Acceleration","authors":"Katerina Tzafilkou, Dimitrios Karapiperis, Vassilios S. Verykios","doi":"10.1109/ISC255366.2022.9921873","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921873","url":null,"abstract":"Mouse tracking can be used as a non-obtrusive data-collection method to identify in real time the users' cognitive and emotional states. Despite the advances in the field, most studies focus on measuring decision conflict processes in typical choice-making tasks, while a framework for emotion prediction in different contexts of web interactions is missing. The present study investigates the potential of measuring a person's negative emotional state through solely mouse cursor data of speed and acceleration. A two study experiment was designed to monitor the mouse behavior of 79 participants in three different types of gaming apps: two gamified campaigns (a puzzle and a hidden-items game), and one Game-based Learning (GBL) quiz task. The collected dataset comprised 123 valid records of mouse features and self-reported emotional statements. A set of different classifiers were trained and tested, where we achieved a maximum accuracy of 81% and 83% for frustration and confusion, respectively. We also achieved higher accuracy, namely 85%, in the case of gamified tasks, excluding the GBL task, implying that further research should be conducted in this field. Our findings indicate that by analyzing speed and acceleration data, it is possible to make efficient predictions of a user's emotional state in different web activities.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123287587","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922359
Chiara Bachechi, Laura Po, Federico Degliangeli
The use of fossil fuels is contributing to the global climate crisis and is threatening the sustainability of the planet. Bicycles are a vital component of the solution, as they can help mitigate the effects of climate change and improve the quality of life for all. However, cities need to be equipped with the necessary infrastructure to support their use guaranteeing safety for cyclists. Moreover, cyclists should plan their route considering the level of security associated with the different available options to reach their destination. The paper tests and presents a method that aims to integrate geographical data from various sources with different geometries and formats into a single view of the cycle paths in the province of Modena, Italy. The Geographic Information System (GIS) software functionalities have been exploited to classify paths in 5 categories: from protected bike lanes to streets with no bike infrastructure. The type of traffic that co-exists in each cycle path was analysed too. The main outcome of this research is a visualization of the cycle paths in the province of Modena highlighting the security of paths, the discontinuity of the routes, and the less covered areas. Moreover, a cycle paths graph data model was generated to perform routing based on the security level.
{"title":"GIS-Based Geospatial Data Analysis: the Security of Cycle Paths in Modena","authors":"Chiara Bachechi, Laura Po, Federico Degliangeli","doi":"10.1109/ISC255366.2022.9922359","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922359","url":null,"abstract":"The use of fossil fuels is contributing to the global climate crisis and is threatening the sustainability of the planet. Bicycles are a vital component of the solution, as they can help mitigate the effects of climate change and improve the quality of life for all. However, cities need to be equipped with the necessary infrastructure to support their use guaranteeing safety for cyclists. Moreover, cyclists should plan their route considering the level of security associated with the different available options to reach their destination. The paper tests and presents a method that aims to integrate geographical data from various sources with different geometries and formats into a single view of the cycle paths in the province of Modena, Italy. The Geographic Information System (GIS) software functionalities have been exploited to classify paths in 5 categories: from protected bike lanes to streets with no bike infrastructure. The type of traffic that co-exists in each cycle path was analysed too. The main outcome of this research is a visualization of the cycle paths in the province of Modena highlighting the security of paths, the discontinuity of the routes, and the less covered areas. Moreover, a cycle paths graph data model was generated to perform routing based on the security level.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123131283","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：