Qinzhi Jiang, Mustafa Naseem, Jamie Lai, Kentaro Toyama, P. Papalambros
Co-design collects insights from multiple stakeholders collaboratively making it a powerful method to design with marginalized populations. In the latter context, stakeholders have varying levels of power causing asymmetry and possible suppression of one group over another. Such power differentials can hinder co-design’s effectiveness. Through thirteen semi-structured interviews with co-design facilitators who have worked with marginalized communities in 43 different countries, we discovered that despite efforts to mitigate power differentials, significant disparities in educational and cultural backgrounds, language barriers, and gender imbalances prevent true collaboration. Tools for prototyping, analysis and evaluation often require literacy, advanced training, and resources. When these are inaccessible, co-design fails to materialize in the design analysis, implementation, and evaluation phases. We found this failure occurred with marginalized groups. We also found that experienced facilitators were aware of their own privilege as well as the power differentials of outside stakeholders such as donors, and they prioritized strategies to address them ahead of time.
{"title":"Understanding Power Differentials and Cultural Differences in Co-design with Marginalized Populations","authors":"Qinzhi Jiang, Mustafa Naseem, Jamie Lai, Kentaro Toyama, P. Papalambros","doi":"10.1145/3530190.3534819","DOIUrl":"https://doi.org/10.1145/3530190.3534819","url":null,"abstract":"Co-design collects insights from multiple stakeholders collaboratively making it a powerful method to design with marginalized populations. In the latter context, stakeholders have varying levels of power causing asymmetry and possible suppression of one group over another. Such power differentials can hinder co-design’s effectiveness. Through thirteen semi-structured interviews with co-design facilitators who have worked with marginalized communities in 43 different countries, we discovered that despite efforts to mitigate power differentials, significant disparities in educational and cultural backgrounds, language barriers, and gender imbalances prevent true collaboration. Tools for prototyping, analysis and evaluation often require literacy, advanced training, and resources. When these are inaccessible, co-design fails to materialize in the design analysis, implementation, and evaluation phases. We found this failure occurred with marginalized groups. We also found that experienced facilitators were aware of their own privilege as well as the power differentials of outside stakeholders such as donors, and they prioritized strategies to address them ahead of time.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127150180","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}
D. Schien, P. Shabajee, James Wickenden, Tristan Warren, C. Preist
We present the EAM toolkit for life cycle modelling and impact analysis in environmental assessments. The open source toolkit was specifically designed to support maintainability and verification of models within integrated assessments, and has been used in research and industry. The tool offers features to support complex Life Cycle Assessment, including dynamic and scenario modelling, uncertainty and sensitivity analysis, a flexible domain specific modelling language and a visual editor. In this introduction we present the main features of the toolkit, summarise the high-level components and illustrate its use.
{"title":"Demo: The EAM Environmental Modelling and Assessment Toolkit","authors":"D. Schien, P. Shabajee, James Wickenden, Tristan Warren, C. Preist","doi":"10.1145/3530190.3542933","DOIUrl":"https://doi.org/10.1145/3530190.3542933","url":null,"abstract":"We present the EAM toolkit for life cycle modelling and impact analysis in environmental assessments. The open source toolkit was specifically designed to support maintainability and verification of models within integrated assessments, and has been used in research and industry. The tool offers features to support complex Life Cycle Assessment, including dynamic and scenario modelling, uncertainty and sensitivity analysis, a flexible domain specific modelling language and a visual editor. In this introduction we present the main features of the toolkit, summarise the high-level components and illustrate its use.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI) and machine learning (ML) are quickly becoming pervasive in ways that impact the lives of all humans across the globe. In an effort to make otherwise ”black box” AI/ML systems more understandable, the field of Explainable AI (XAI) has arisen with the goal of developing algorithms, toolkits, frameworks, and other techniques that enable people to comprehend, trust, and manage AI systems. However, although XAI is a rapidly growing area of research, most of the work has focused on contexts in the Global North, and little is known about if or how XAI techniques have been designed, deployed, or tested with communities in the Global South. This gap is concerning, especially in light of rapidly growing enthusiasm from governments, companies, and academics to use AI/ML to “solve” problems in the Global South. Our paper contributes the first systematic review of XAI research in the Global South, providing an early look at emerging work in the space. We identified 16 papers from 15 different venues that targeted a wide range of application domains. All of the papers were published in the last three years. Of the 16 papers, 13 focused on applying a technical XAI method, all of which involved the use of (at least some) data that was local to the context. However, only three papers engaged with or involved humans in the work, and only one attempted to deploy their XAI system with target users. We close by reflecting on the current state of XAI research in the Global South, discussing data and model considerations for building and deploying XAI systems in these regions, and highlighting the need for human-centered approaches to XAI in the Global South.
{"title":"Making AI Explainable in the Global South: A Systematic Review","authors":"Chinasa T. Okolo, Nicola Dell, Aditya Vashistha","doi":"10.1145/3530190.3534802","DOIUrl":"https://doi.org/10.1145/3530190.3534802","url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) are quickly becoming pervasive in ways that impact the lives of all humans across the globe. In an effort to make otherwise ”black box” AI/ML systems more understandable, the field of Explainable AI (XAI) has arisen with the goal of developing algorithms, toolkits, frameworks, and other techniques that enable people to comprehend, trust, and manage AI systems. However, although XAI is a rapidly growing area of research, most of the work has focused on contexts in the Global North, and little is known about if or how XAI techniques have been designed, deployed, or tested with communities in the Global South. This gap is concerning, especially in light of rapidly growing enthusiasm from governments, companies, and academics to use AI/ML to “solve” problems in the Global South. Our paper contributes the first systematic review of XAI research in the Global South, providing an early look at emerging work in the space. We identified 16 papers from 15 different venues that targeted a wide range of application domains. All of the papers were published in the last three years. Of the 16 papers, 13 focused on applying a technical XAI method, all of which involved the use of (at least some) data that was local to the context. However, only three papers engaged with or involved humans in the work, and only one attempted to deploy their XAI system with target users. We close by reflecting on the current state of XAI research in the Global South, discussing data and model considerations for building and deploying XAI systems in these regions, and highlighting the need for human-centered approaches to XAI in the Global South.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114170904","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}
Researchers heavily use online platforms for collecting trace data, i.e., data capturing user interaction on and with sociotechnical systems. Human-computer interaction scholars have highlighted the role of reflexivity while analyzing such data in the case of marginalized communities. Drawing on sociomaterial perspectives, we highlight how data collection approaches involving lists of search phrases and APIs can embed researchers’ positionality, perspectives, and biases within the datasets. In this note, we reflect on the data collection approaches of two papers that studied the sociohistorically marginalized Bengali communities on the question-and-answer site Bengali Quora. We illustrate how recommendation systems and data labeling workers can be included in the data collection process to democratize and limit bias while broadening and contextualizing the trace datasets for research.
{"title":"Note: A Sociomaterial Perspective on Trace Data Collection: Strategies for Democratizing and Limiting Bias","authors":"Dipto Das, Arpon Podder, Bryan C. Semaan","doi":"10.1145/3530190.3534835","DOIUrl":"https://doi.org/10.1145/3530190.3534835","url":null,"abstract":"Researchers heavily use online platforms for collecting trace data, i.e., data capturing user interaction on and with sociotechnical systems. Human-computer interaction scholars have highlighted the role of reflexivity while analyzing such data in the case of marginalized communities. Drawing on sociomaterial perspectives, we highlight how data collection approaches involving lists of search phrases and APIs can embed researchers’ positionality, perspectives, and biases within the datasets. In this note, we reflect on the data collection approaches of two papers that studied the sociohistorically marginalized Bengali communities on the question-and-answer site Bengali Quora. We illustrate how recommendation systems and data labeling workers can be included in the data collection process to democratize and limit bias while broadening and contextualizing the trace datasets for research.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114697181","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}
India has been witnessing a steady increase in smartphone penetration since 2016 after Reliance Jio introduced inexpensive internet plans. Much of HCI research in the Global South has been conducted before smartphones became more widespread. More recent work on smartphone use in India has been either domain-focused or studied specific features. In this work, we investigate how emergent users from low-income communities in India currently use their smartphones, and what they use them for. We draw on semi-structured interviews with emergent smartphone users across rural and urban India demonstrating their experiences and challenges related to low- textual and digital literacy, infrastructure, privacy, and motivations of use. Our findings revealed that while there is a lack of understanding of basic features such as accounts and passwords, there is sophisticated use spanning user-generated media, remote education, skilling, etc. We close with recommendations for future research and design for emergent smartphone users.
{"title":"Sophistication with Limitation: Understanding Smartphone Usage by Emergent Users in India","authors":"Meghna Gupta, Devansh Mehta, Anandita Punj, Indrani Medhi-Thies","doi":"10.1145/3530190.3534824","DOIUrl":"https://doi.org/10.1145/3530190.3534824","url":null,"abstract":"India has been witnessing a steady increase in smartphone penetration since 2016 after Reliance Jio introduced inexpensive internet plans. Much of HCI research in the Global South has been conducted before smartphones became more widespread. More recent work on smartphone use in India has been either domain-focused or studied specific features. In this work, we investigate how emergent users from low-income communities in India currently use their smartphones, and what they use them for. We draw on semi-structured interviews with emergent smartphone users across rural and urban India demonstrating their experiences and challenges related to low- textual and digital literacy, infrastructure, privacy, and motivations of use. Our findings revealed that while there is a lack of understanding of basic features such as accounts and passwords, there is sophisticated use spanning user-generated media, remote education, skilling, etc. We close with recommendations for future research and design for emergent smartphone users.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"16 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180405","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}
E. Schoemaker, Reem Talhouk, C. Kamanu, Eoghan McDonaugh, Christian Mcdonaugh, Eliza Casey, Adam Wills, Finn Richardson, J. Donner
This paper examines the experiences and perspectives of Kenyans who use social media platforms as part of their agricultural livelihoods. Through a mixed-methods study of 324 survey respondents and 81 interviews, we present data that demonstrates the significance and shape of “social agriculture” in the Kenyan agricultural landscape. We complement previous ICT4D/HCI4D literature that has primarily focused on purpose-built agricultural platforms through a novel focus on farmers’ appropriation of existing social media platforms to enter the agricultural sector and diversify agricultural livelihoods. Our study highlights new insights into the growing phenomenon of using social media platforms for agriculture practice, including how these platforms afford particular practices around the buying and selling of produce and information on social media platforms. We also identify challenges around trust and online abuse and describe the strategies employed by participants to counter them. Lastly, we build on our findings to highlight the affordances and constraints of using social media platforms, thus contributing to the field an initial conceptualization of social agriculture as a space of commerce. We offer eight design considerations for both technology designers and international development stakeholders to strengthen the potential for social platforms to afford social agricultural practices that enrich individual lives and livelihoods.
{"title":"Social Agriculture: Examining the Affordances of Social Media for Agricultural Practices","authors":"E. Schoemaker, Reem Talhouk, C. Kamanu, Eoghan McDonaugh, Christian Mcdonaugh, Eliza Casey, Adam Wills, Finn Richardson, J. Donner","doi":"10.1145/3530190.3534806","DOIUrl":"https://doi.org/10.1145/3530190.3534806","url":null,"abstract":"This paper examines the experiences and perspectives of Kenyans who use social media platforms as part of their agricultural livelihoods. Through a mixed-methods study of 324 survey respondents and 81 interviews, we present data that demonstrates the significance and shape of “social agriculture” in the Kenyan agricultural landscape. We complement previous ICT4D/HCI4D literature that has primarily focused on purpose-built agricultural platforms through a novel focus on farmers’ appropriation of existing social media platforms to enter the agricultural sector and diversify agricultural livelihoods. Our study highlights new insights into the growing phenomenon of using social media platforms for agriculture practice, including how these platforms afford particular practices around the buying and selling of produce and information on social media platforms. We also identify challenges around trust and online abuse and describe the strategies employed by participants to counter them. Lastly, we build on our findings to highlight the affordances and constraints of using social media platforms, thus contributing to the field an initial conceptualization of social agriculture as a space of commerce. We offer eight design considerations for both technology designers and international development stakeholders to strengthen the potential for social platforms to afford social agricultural practices that enrich individual lives and livelihoods.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131586112","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}
Amrita Gupta, Tony Chang, Jeffrey Walker, B. Letcher
Effective water resources management depends on monitoring the volume of water flowing through streams and rivers, but collecting continuous discharge measurements using traditional streamflow gages is prohibitively expensive. Time-lapse cameras offer a low-cost option for streamflow monitoring, but training models for predicting streamflow directly from images requires streamflow data to use as labels, which are often unavailable. We address this data gap by proposing the alternative task of Streamflow Rank Estimation (SRE), in which the goal is to predict relative measures of streamflow such as percentile rank rather than absolute flow. In particular, we use a learning-to-rank framework to train SRE models using pairs of stream images ranked in order of discharge by an annotator, obviating the need for discharge training data and thus facilitating monitoring streamflow conditions at streams without gages. We also demonstrate a technique for converting SRE model predictions to stream discharge estimates given an estimated streamflow distribution. Using data and images from six small US streams, we compare the performance of SRE with conventional regression models trained to predict absolute discharge. Our results show that SRE performs nearly as well as regression models on relative flow prediction. Further, we observe that the accuracy of absolute discharge estimates obtained by mapping SRE model predictions through a discharge distribution largely depends on how well the assumed discharge distribution matches the field observed data.
{"title":"Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning","authors":"Amrita Gupta, Tony Chang, Jeffrey Walker, B. Letcher","doi":"10.1145/3530190.3534805","DOIUrl":"https://doi.org/10.1145/3530190.3534805","url":null,"abstract":"Effective water resources management depends on monitoring the volume of water flowing through streams and rivers, but collecting continuous discharge measurements using traditional streamflow gages is prohibitively expensive. Time-lapse cameras offer a low-cost option for streamflow monitoring, but training models for predicting streamflow directly from images requires streamflow data to use as labels, which are often unavailable. We address this data gap by proposing the alternative task of Streamflow Rank Estimation (SRE), in which the goal is to predict relative measures of streamflow such as percentile rank rather than absolute flow. In particular, we use a learning-to-rank framework to train SRE models using pairs of stream images ranked in order of discharge by an annotator, obviating the need for discharge training data and thus facilitating monitoring streamflow conditions at streams without gages. We also demonstrate a technique for converting SRE model predictions to stream discharge estimates given an estimated streamflow distribution. Using data and images from six small US streams, we compare the performance of SRE with conventional regression models trained to predict absolute discharge. Our results show that SRE performs nearly as well as regression models on relative flow prediction. Further, we observe that the accuracy of absolute discharge estimates obtained by mapping SRE model predictions through a discharge distribution largely depends on how well the assumed discharge distribution matches the field observed data.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131250875","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}
Ishrat Jahan Eliza, Md. Hasibul Hussain Hisham, Mohammad Nuwaisir Rahman, A. Akil, Abir Mohammad Turza, Fahim Morshed, Nazmus Sakib, S. Chellappan, A. Islam
Epidemics and pandemics have been affecting human lives since time, and have sometimes altered the course of history. At this very moment, Coronavirus (COVID-19) pandemic has been the defining global health crisis. Now, perhaps for the first time in history, humanity as a whole has undergone major disruptions to life and some form of lockdown. New policies need to be forged by policy-makers for various sectors such as trading, banking, education, etc., to lessen losses and to heal quickly. For efficient policy-making, in turn, some prerequisites needed are historical trend analysis on the pandemic spread, future forecasting, the correlation between the spread of the disease and various socio-economic and environmental factors, etc. Besides, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy-making. Therefore, in this work, we developed a web-based integrated real-time operational dashboard as a one-stop decision support system for COVID-19. In our study, we conducted a detailed data-driven analysis based on available data from multiple authenticated sources to predict the upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored the correlations between disease spread and diverse socio-economic as well as environmental factors. Furthermore, we presented how the outcomes of our work can facilitate both contemporary and future policy-making.
{"title":"Note: CORONOSIS: Corona Prognosis via a Global Lens to Enable Efficient Policy-making Both at Global and Local Levels","authors":"Ishrat Jahan Eliza, Md. Hasibul Hussain Hisham, Mohammad Nuwaisir Rahman, A. Akil, Abir Mohammad Turza, Fahim Morshed, Nazmus Sakib, S. Chellappan, A. Islam","doi":"10.1145/3530190.3534831","DOIUrl":"https://doi.org/10.1145/3530190.3534831","url":null,"abstract":"Epidemics and pandemics have been affecting human lives since time, and have sometimes altered the course of history. At this very moment, Coronavirus (COVID-19) pandemic has been the defining global health crisis. Now, perhaps for the first time in history, humanity as a whole has undergone major disruptions to life and some form of lockdown. New policies need to be forged by policy-makers for various sectors such as trading, banking, education, etc., to lessen losses and to heal quickly. For efficient policy-making, in turn, some prerequisites needed are historical trend analysis on the pandemic spread, future forecasting, the correlation between the spread of the disease and various socio-economic and environmental factors, etc. Besides, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy-making. Therefore, in this work, we developed a web-based integrated real-time operational dashboard as a one-stop decision support system for COVID-19. In our study, we conducted a detailed data-driven analysis based on available data from multiple authenticated sources to predict the upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored the correlations between disease spread and diverse socio-economic as well as environmental factors. Furthermore, we presented how the outcomes of our work can facilitate both contemporary and future policy-making.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127059101","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}
Phuthipong Bovornkeeratiroj, John Wamburu, David E. Irwin, P. Shenoy
As the electric grid undergoes the transition to a carbon free future, many new techniques for optimizing the grid’s energy usage and carbon footprint are being designed. A common technique used by many approaches is to reduce the energy usage of the grid’s peak demand periods since doing so is beneficial for reducing the carbon usage of the grid. Consequently, the design of peak forecasting methods that predict when and how much peak demand will be seen is at the heart of many energy optimization approaches. In this paper, we present PeakTK, an open-source toolkit and reference datasets for peak forecasting in energy systems. PeakTK implements a range of peak forecasting methods that have been proposed recently and exposes them through well-defined interfaces and library modules. Our goal is to improve reproducibility of energy systems research by providing a common framework for evaluating and comparing new peak forecasting algorithms. Further, PeakTK provides libraries to enable researchers and practitioners to easily incorporate peak forecasting methods into their research when implementing higher level grid optimizations. We discuss the design and implementation of PeakTK and present case studies to demonstrate how PeakTK can be used for forecasting or quantitative comparisons of energy optimization methods.
{"title":"PeakTK: An Open Source Toolkit for Peak Forecasting in Energy Systems","authors":"Phuthipong Bovornkeeratiroj, John Wamburu, David E. Irwin, P. Shenoy","doi":"10.1145/3530190.3534791","DOIUrl":"https://doi.org/10.1145/3530190.3534791","url":null,"abstract":"As the electric grid undergoes the transition to a carbon free future, many new techniques for optimizing the grid’s energy usage and carbon footprint are being designed. A common technique used by many approaches is to reduce the energy usage of the grid’s peak demand periods since doing so is beneficial for reducing the carbon usage of the grid. Consequently, the design of peak forecasting methods that predict when and how much peak demand will be seen is at the heart of many energy optimization approaches. In this paper, we present PeakTK, an open-source toolkit and reference datasets for peak forecasting in energy systems. PeakTK implements a range of peak forecasting methods that have been proposed recently and exposes them through well-defined interfaces and library modules. Our goal is to improve reproducibility of energy systems research by providing a common framework for evaluating and comparing new peak forecasting algorithms. Further, PeakTK provides libraries to enable researchers and practitioners to easily incorporate peak forecasting methods into their research when implementing higher level grid optimizations. We discuss the design and implementation of PeakTK and present case studies to demonstrate how PeakTK can be used for forecasting or quantitative comparisons of energy optimization methods.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117008349","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}
Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) put efforts for management, planning, and optimization; however, they do not link such activities to socioeconomic fairness. In this paper, we make a first step towards understanding the relation between socioeconomic status of customers and network performance, and investigate potential discrimination in network deployment and management. The scope of our study spans various aspects, including urban geography, network resource deployment, data consumption, and device distribution. A novel methodology that enables a geo-socioeconomic perspective to mobile network is developed for the study. The results are based on an actual infrastructure in multiple cities, covering millions of users densely covering the socioeconomic scale. We report a thorough examination of the fairness status, its relationship with various structural factors, and potential class specific solutions.
{"title":"A Large-scale Examination of ”Socioeconomic” Fairness in Mobile Networks","authors":"Souneil Park, Pavol Mulinka, Diego Perino","doi":"10.1145/3530190.3534809","DOIUrl":"https://doi.org/10.1145/3530190.3534809","url":null,"abstract":"Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) put efforts for management, planning, and optimization; however, they do not link such activities to socioeconomic fairness. In this paper, we make a first step towards understanding the relation between socioeconomic status of customers and network performance, and investigate potential discrimination in network deployment and management. The scope of our study spans various aspects, including urban geography, network resource deployment, data consumption, and device distribution. A novel methodology that enables a geo-socioeconomic perspective to mobile network is developed for the study. The results are based on an actual infrastructure in multiple cities, covering millions of users densely covering the socioeconomic scale. We report a thorough examination of the fairness status, its relationship with various structural factors, and potential class specific solutions.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124642423","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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