2022 IEEE Global Humanitarian Technology Conference (GHTC)最新文献_第2页

Motorcycle and Vehicle Detection for Applications in Road Safety and Traffic Monitoring Systems 摩托车和车辆检测在道路安全和交通监控系统中的应用

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910992

G. Cruz, Aaron Litonjua, Alysia Noreen P. San Juan, Nathaniel J. C. Libatique, Marion Ivan L. Tan, J. L. E. Honrado

引用次数: 1

Remote Crop Disease Detection Using Deep Learning with IoT 基于物联网的深度学习远程作物病害检测

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910991

Ivy Chung, Anoushka Gupta, T. Ogunfunmi

引用次数: 2

Assessment of Emissions with Carbon-smart Farming Practices and Participatory Sensing in Rice 碳智能农业实践和参与式感知对水稻排放的评估

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910612

R. Kulat, Mariappan Sakkan, Prachin Jain, Sanat Sarangi, S. Pappula

{"title":"Assessment of Emissions with Carbon-smart Farming Practices and Participatory Sensing in Rice","authors":"R. Kulat, Mariappan Sakkan, Prachin Jain, Sanat Sarangi, S. Pappula","doi":"10.1109/GHTC55712.2022.9910612","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9910612","url":null,"abstract":"Agriculture sector is a significant contributor to greenhouse gas (GHG) emissions especially in the conventional rice ecosystem with carbon-insensitive practices. In this study, we assess the carbon footprint of selected farms based on GHG emissions and carbon sequestration from recommended agricultural practices with a human participatory sensing approach. A set of ten selected farmers was split into two groups and asked to follow carbon-smart crop protocols (CSCP) called CSCP-1 and CSCP-2. With the digitally captured record of operations, process modelling was used to simulate the CSCP scenarios followed on the ground, and a classification model was developed to estimate the Nitrogen uptake to improve fertilizer utilization for farmers. For various potential scenarios involving variation in irrigation and fertilizer application, impact on GHG emissions and SOC dynamics was evaluated. Results showed that CSCP-1 farmers emitted more GHGs when compared to CSCP-2 farmers while they also sequestrated more carbon in comparison with no significant difference in Net GWP (Global Warming Potential). CSCP farmers with both flood irrigation and furrow irrigation sequestrated more carbon than farmers who would follow conventional practices. Net GWP of CSCP farmers was significantly lower than conventional farmers indicating carbon-smart practices can indeed make a significant difference in sustainability initiatives.","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128833091","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}

引用次数: 0

Towards Generating Contextual and Empathetic Response for Covid-related Queries 为与新冠病毒相关的查询生成上下文和同理心的响应

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910984

Sowmya Rasipuram, Anutosh Maitra, Bishal Shaw, S. Saha

{"title":"Towards Generating Contextual and Empathetic Response for Covid-related Queries","authors":"Sowmya Rasipuram, Anutosh Maitra, Bishal Shaw, S. Saha","doi":"10.1109/GHTC55712.2022.9910984","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9910984","url":null,"abstract":"This work addresses the vital need of keeping people informed with relevant, correct and essential information during the pandemic. Advanced NLP and machine learning mechanisms have been leveraged to generate responses to user queries through contextual conversation. In order to help people be discerning about what information they receive, a conversational system is proposed that identifies the correct intent of the query and a reinforcement Learning based generation model is used to proceed with conversation. We propose an end-to-end real-time text generation model that can respond to users queries on covid19. We created a new dataset with 1200+ covid-related questions from various sources and pre-processed them for a brief and direct answer. The dataset has also been manually observed to identify depressed questions and the responses are converted to be more empathetic. The dataset has been used to fine-tune DailoGPT, a GPT2-based transformer model to generate the responses related to COVID. COVID-related queries are bucketed into 15 categories to identify the exact intent of people. Our model generated both contextual and empathetic responses and achieved a human evaluation score of 3.48 (on a scale of 5) in terms of contextual relevance and a score of 2.12 (on a scale of 3).","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131106262","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}

引用次数: 0

Prioritizing emergency evacuations under compounding levels of uncertainty 在复杂的不确定程度下确定紧急疏散的优先次序

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910611

Lisa J. Einstein, Robert J. Moss, Mykel J. Kochenderfer

{"title":"Prioritizing emergency evacuations under compounding levels of uncertainty","authors":"Lisa J. Einstein, Robert J. Moss, Mykel J. Kochenderfer","doi":"10.1109/GHTC55712.2022.9910611","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9910611","url":null,"abstract":"Well-executed emergency evacuations can save lives and reduce suffering. However, decision makers struggle to determine optimal evacuation policies given the chaos, uncertainty, and value judgments inherent in emergency evacuations. We propose and analyze a decision support tool for pre-crisis training exercises for teams preparing for civilian evacuations and explore the tool in the case of the 2021 U.S.-led evacuation from Afghanistan. We use different classes of Markov decision processes (MDPs) to capture compounding levels of uncertainty in (1) the priority category of who appears next at the gate for evacuation, (2) the distribution of priority categories at the population level, and (3) individuals’ claimed priority category. We compare the number of people evacuated by priority status under eight heuristic policies. The optimized MDP policy achieves the best performance compared to all heuristic baselines. We also show that accounting for the compounding levels of model uncertainty incurs added complexity without improvement in policy performance. Useful heuristics can be extracted from the optimized policies to inform human decision makers. We opensource all tools to encourage robust dialogue about the trade-offs, limitations, and potential of integrating algorithms into high-stakes humanitarian decision-making.","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133899341","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}

引用次数: 0

GHTC 2022 Program GHTC 2022计划

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/ghtc55712.2022.9911010

引用次数: 0

The Challenge and Value of Dashboard Development During the COVID-19 Pandemic COVID-19大流行期间仪表板开发的挑战与价值

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910610

Kenny Meesters, J.M.M. Wijnen, Mats Visser, K. Boersma

引用次数: 0

Selection of multicriteria decision analysis methods for electrification projects in rural Sub-Sahara Africa – A case study in Niger 撒哈拉以南非洲农村电气化项目多标准决策分析方法的选择——以尼日尔为例

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9911026

Julian Huwer, Georg Frey, R. Bhandari

引用次数: 0

EcoRealms: Improving Well-being and Enhancing Productivity Through Incorporating Nature Into the Workplace 生态王国:通过将自然融入工作场所来改善幸福感和提高生产力

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9911004

Evy M. Rahmey, Giavanna Gast, Sebastian R. Wick, Tram U. H. Vo, Margaret A. McLaughlin, Jonathan Osika, Brian Slocum, Nicholas Sawicki, Khanjan Mehta

{"title":"EcoRealms: Improving Well-being and Enhancing Productivity Through Incorporating Nature Into the Workplace","authors":"Evy M. Rahmey, Giavanna Gast, Sebastian R. Wick, Tram U. H. Vo, Margaret A. McLaughlin, Jonathan Osika, Brian Slocum, Nicholas Sawicki, Khanjan Mehta","doi":"10.1109/GHTC55712.2022.9911004","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9911004","url":null,"abstract":"Work-induced stress is a large problem that has only been exacerbated by the coronavirus pandemic. Nature has beneficial effects on psychological and physiological well-being, with an abundance of scientific literature demonstrating the ability of greenery to reduce stress. As such, the fusion of nature-based design into the work and academic environments has the potential to greatly decrease student and employee stress. Primary methods of incorporating greenery indoors include living walls and potted plants. However, these methods fall short of creating an immersive environment that maximizes the positive impact on worker well-being, and additionally, barriers such as maintenance, costs, and extensive construction limit implementation. This paper outlines a new method to integrate nature into the work environment through “EcoRealms,” which are immersive, natural spaces created by modular and self-maintaining ‘living partitions.’ These low-cost, easy-to-install partitions act as design elements to create a flexible space that serves worker well-being and enhances productivity. Discussed are prototypes that demonstrate the design’s technical feasibility and results from a self-reported questionnaire that validate the positive impacts of the EcoRealm on wellbeing.","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133615295","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}

引用次数: 0

A Machine Learning-Based Automatic Feedback System to Teach Cybersecurity Principles to K-12 and College Students 基于机器学习的自动反馈系统向K-12和大学生教授网络安全原理

2022 IEEE Global Humanitarian Technology Conference (GHTC) Pub Date : 2022-09-08 DOI: 10.1109/GHTC55712.2022.9910998

Eric M. Dillon, Craig Carpenter, John Cook, Thomas D. Wills, Husnu S. Narman

{"title":"A Machine Learning-Based Automatic Feedback System to Teach Cybersecurity Principles to K-12 and College Students","authors":"Eric M. Dillon, Craig Carpenter, John Cook, Thomas D. Wills, Husnu S. Narman","doi":"10.1109/GHTC55712.2022.9910998","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9910998","url":null,"abstract":"Feedback is an essential part of education to help students understand and learn from their mistakes. However, while students learn new content, there is mostly no live person to provide feedback, especially in a virtual environment. Therefore, there are many software for automated code reviews to provide feedback to programming language learners. However, there are no available auto command review tools for security tools except for each tool itself and operating system suggestions. There is also no feedback tool that constructively provides feedback according to learners’ experiences in security subjects while learners practice with commands. Therefore, we developed an automatic feedback system that uses machine learning to create customized student feedback on cybersecurity topics. The foundation of the software was completed and tested in 2 undergraduate introductory computer science courses. Survey results collected from users indicate that the automatic feedback system improved the learning experience of 46% of successful participants and that 77% of successful participants were interested in the continued development of the system. 88% of successful participants felt that the system taught basic command-line skills effectively.","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131208282","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}

引用次数: 1