Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3432132
Colin Ashruf
{"title":"The Ethics of Product Development—Houston, Do We Have a Problem?","authors":"Colin Ashruf","doi":"10.1109/MTS.2024.3432132","DOIUrl":"https://doi.org/10.1109/MTS.2024.3432132","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"31-36"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3465114
{"title":"IEEE Connects You to a Universe of Information!","authors":"","doi":"10.1109/MTS.2024.3465114","DOIUrl":"https://doi.org/10.1109/MTS.2024.3465114","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"36-36"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3455388
Ketra Schmitt
{"title":"Food Security and Agriculture: Technology, Policy, Choices","authors":"Ketra Schmitt","doi":"10.1109/MTS.2024.3455388","DOIUrl":"https://doi.org/10.1109/MTS.2024.3455388","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"8-18"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3455389
Luis Kun
{"title":"Technology, Society, and Generational Interoperability—An Incredible July 2024","authors":"Luis Kun","doi":"10.1109/MTS.2024.3455389","DOIUrl":"https://doi.org/10.1109/MTS.2024.3455389","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"4-7"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704943","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3468490
{"title":"TechRxiv","authors":"","doi":"10.1109/MTS.2024.3468490","DOIUrl":"https://doi.org/10.1109/MTS.2024.3468490","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"100-100"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3410307
Evagelia Emily Tavoulareas
{"title":"The Gap Between Policy and Implementation Has Roots in Academia: How Policy Schools Can Narrow the Gap","authors":"Evagelia Emily Tavoulareas","doi":"10.1109/MTS.2024.3410307","DOIUrl":"https://doi.org/10.1109/MTS.2024.3410307","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"26-30"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704946","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/MTS.2024.3457100
{"title":"Understanding the Role of Technology in Society","authors":"","doi":"10.1109/MTS.2024.3457100","DOIUrl":"https://doi.org/10.1109/MTS.2024.3457100","url":null,"abstract":"","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"C4-C4"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1109/MTS.2024.3457679
Mariana Zafeirakopoulos
Intelligence analysis provides decision support in national security contexts. The current approach to supported decision-making tends toward reductivism and analysis, regardless of the type of national security issue. Currently, there is little research available on the alternative approaches and practices needed for intervening in national security contexts that are emerging and, therefore, not well understood. Consequently, this article explores the idea that a homogenous approach to national security problem-solving is insufficient, and we suggest that different national security issues require different approaches. In this article, we apply practices of exploration, relationality, and participation from the field of design as established approaches to sensemaking. We offer sensemaking as an alternative to reductive analytic thinking by applying it to a national security issue: the role of artificial intelligence (AI) in cybersecurity. To explore sensemaking, six workshops were conducted over six months in 2021. These workshops used design practices (thinking and tools) to explore AI in cybersecurity. From studying the workshop activities and analyzing interviews conducted by the core design team (CDT) (Project Steering Group), the study’s findings suggest new practices for Intelligence to support decision-making in future-oriented contexts. These practices include using design tools such as personas and scenarios to anchor the exploration of future harms, which also give legitimacy to lived experience alongside expert knowledge. This study also identifies possibilities for future engagement, participation, and dialog between government functions such as Intelligence and civil society to explore unknown and emerging issues together. Consequently, a relational approach gives legitimacy to seemingly unconventional ways of thinking, approaching, and knowing about future unknown contexts.
{"title":"Sensemaking National Security: Applying Design Practice to Explore AI in Cybersecurity","authors":"Mariana Zafeirakopoulos","doi":"10.1109/MTS.2024.3457679","DOIUrl":"https://doi.org/10.1109/MTS.2024.3457679","url":null,"abstract":"Intelligence analysis provides decision support in national security contexts. The current approach to supported decision-making tends toward reductivism and analysis, regardless of the type of national security issue. Currently, there is little research available on the alternative approaches and practices needed for intervening in national security contexts that are emerging and, therefore, not well understood. Consequently, this article explores the idea that a homogenous approach to national security problem-solving is insufficient, and we suggest that different national security issues require different approaches. In this article, we apply practices of exploration, relationality, and participation from the field of design as established approaches to sensemaking. We offer sensemaking as an alternative to reductive analytic thinking by applying it to a national security issue: the role of artificial intelligence (AI) in cybersecurity. To explore sensemaking, six workshops were conducted over six months in 2021. These workshops used design practices (thinking and tools) to explore AI in cybersecurity. From studying the workshop activities and analyzing interviews conducted by the core design team (CDT) (Project Steering Group), the study’s findings suggest new practices for Intelligence to support decision-making in future-oriented contexts. These practices include using design tools such as personas and scenarios to anchor the exploration of future harms, which also give legitimacy to lived experience alongside expert knowledge. This study also identifies possibilities for future engagement, participation, and dialog between government functions such as Intelligence and civil society to explore unknown and emerging issues together. Consequently, a relational approach gives legitimacy to seemingly unconventional ways of thinking, approaching, and knowing about future unknown contexts.","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 4","pages":"72-82"},"PeriodicalIF":2.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/MTS.2024.3455110
François Xavier Sikounmo;Cedric Deffo;Clémentin Tayou Djamegni
Plant leaf infections are a common threat to global production in both the long and short terms, affecting not only many farmers but also consumers around the world. Early detection and treatment of plant leaf diseases are essential to promote healthy plant growth in agriculture and ensure sufficient supply and health security for the world’s geometric (population) growth. Detection of plant leaf diseases using computer-aided technologies is widespread today. In the first part of this thesis, we describe an artificial intelligence (AI) model that enables image analysis to facilitate disease detection and then present its contribution at the societal level. We used images of maize leaves and images of apples in fields from the standard PlantVillage repository for object localization. An efficient deep learning (DL) modified mask region convolutional neural network (Mask R-CNN) is proposed for autonomous segmentation and detection of the object to be analyzed in this research. The proposed work exploited the features learned by the Mask R-CNN model at various processing hierarchies. We achieved improved code generation of singular images of the detected objects and an overall accuracy of 98.89% on the validation sets. In the rest of our research, we wanted to show the impact of our solution at a social level while highlighting the important aspects that characterize good development. The specificity of this approach is to present the different AI solutions used for the analysis of agricultural crops, with the aim of highlighting their benefits and their impact on human activities.
{"title":"Social and Environmental Impact of a Plant Disease Analysis Method Based on Object Extraction","authors":"François Xavier Sikounmo;Cedric Deffo;Clémentin Tayou Djamegni","doi":"10.1109/MTS.2024.3455110","DOIUrl":"https://doi.org/10.1109/MTS.2024.3455110","url":null,"abstract":"Plant leaf infections are a common threat to global production in both the long and short terms, affecting not only many farmers but also consumers around the world. Early detection and treatment of plant leaf diseases are essential to promote healthy plant growth in agriculture and ensure sufficient supply and health security for the world’s geometric (population) growth. Detection of plant leaf diseases using computer-aided technologies is widespread today. In the first part of this thesis, we describe an artificial intelligence (AI) model that enables image analysis to facilitate disease detection and then present its contribution at the societal level. We used images of maize leaves and images of apples in fields from the standard PlantVillage repository for object localization. An efficient deep learning (DL) modified mask region convolutional neural network (Mask R-CNN) is proposed for autonomous segmentation and detection of the object to be analyzed in this research. The proposed work exploited the features learned by the Mask R-CNN model at various processing hierarchies. We achieved improved code generation of singular images of the detected objects and an overall accuracy of 98.89% on the validation sets. In the rest of our research, we wanted to show the impact of our solution at a social level while highlighting the important aspects that characterize good development. The specificity of this approach is to present the different AI solutions used for the analysis of agricultural crops, with the aim of highlighting their benefits and their impact on human activities.","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"65-71"},"PeriodicalIF":2.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Standard farming procedures have been enhanced with the integration of information and communication technologies (ICTs), such as sensors and wireless sensor networks (WSNs), to improve efficiency. This study delves into the observations derived from comments made on YouTube channels pertaining to the topic of smart farming. We further investigate the utilization of machine learning techniques to automate the analysis of comments. In addition, this work utilizes four feature vectorization techniques and nine machine learning models to perform sentiment analysis on a data set of comments. The support vector machine radial basis function (SVM-R) classifier, when combined with the term frequency (TF) vectorizer, gets the highest macro-F1 score of 0.6683. The explainable artificial intelligence (XAI) technique, called local interpretable model-agnostic explanations (LIMEs), has been utilized to gain insights into the outcomes of the highest-performing model.
{"title":"Harvesting Insights: Sentiment Analysis on Smart Farming YouTube Comments for User Engagement and Agricultural Innovation","authors":"Abhishek Kaushik;Sargam Yadav;Shubham Sharma;Kevin McDaid","doi":"10.1109/MTS.2024.3455754","DOIUrl":"https://doi.org/10.1109/MTS.2024.3455754","url":null,"abstract":"Standard farming procedures have been enhanced with the integration of information and communication technologies (ICTs), such as sensors and wireless sensor networks (WSNs), to improve efficiency. This study delves into the observations derived from comments made on YouTube channels pertaining to the topic of smart farming. We further investigate the utilization of machine learning techniques to automate the analysis of comments. In addition, this work utilizes four feature vectorization techniques and nine machine learning models to perform sentiment analysis on a data set of comments. The support vector machine radial basis function (SVM-R) classifier, when combined with the term frequency (TF) vectorizer, gets the highest macro-F1 score of 0.6683. The explainable artificial intelligence (XAI) technique, called local interpretable model-agnostic explanations (LIMEs), has been utilized to gain insights into the outcomes of the highest-performing model.","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"43 3","pages":"91-100"},"PeriodicalIF":2.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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