Pub Date : 2022-07-26DOI: 10.48550/arXiv.2207.12662
Guangyao Dou, Zheng Zhou, Xiaodong Qu
. Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advanc-ing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.
{"title":"Time Majority Voting, a PC-based EEG Classifier for Non-expert Users","authors":"Guangyao Dou, Zheng Zhou, Xiaodong Qu","doi":"10.48550/arXiv.2207.12662","DOIUrl":"https://doi.org/10.48550/arXiv.2207.12662","url":null,"abstract":". Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advanc-ing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122480724","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 : 2022-06-19DOI: 10.1007/978-3-031-19679-9_87
Antonios Saravanos, Stavros Zervoudakis, Dongnanzi Zheng, Amarpreet Nanda, Georgios Shaheen, Charles Hornat, Jeremiah Konde Chaettle, Alassane Yoda, Hyeree Park, Will Ang
{"title":"Reputation, Risk, and Trust on User Adoption of Internet Search Engines: The Case of DuckDuckGo","authors":"Antonios Saravanos, Stavros Zervoudakis, Dongnanzi Zheng, Amarpreet Nanda, Georgios Shaheen, Charles Hornat, Jeremiah Konde Chaettle, Alassane Yoda, Hyeree Park, Will Ang","doi":"10.1007/978-3-031-19679-9_87","DOIUrl":"https://doi.org/10.1007/978-3-031-19679-9_87","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121927656","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 : 2022-06-16DOI: 10.1007/978-3-031-05939-1_25
Alexander Schäfer, Gerd Reis, D. Stricker
{"title":"Learning Effect of Lay People in Gesture-Based Locomotion in Virtual Reality","authors":"Alexander Schäfer, Gerd Reis, D. Stricker","doi":"10.1007/978-3-031-05939-1_25","DOIUrl":"https://doi.org/10.1007/978-3-031-05939-1_25","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134480999","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 : 2022-06-15DOI: 10.48550/arXiv.2206.07442
Rishabh Vallabh Varsha Haria, Sahar Mahdie Klim Al Zaidawi, S. Maneth
In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it is around 4.1 %); this is achieved by varying the number of participants. And second, we are able to provide a mean accuracy with a very low standard error (SEM): our accuracy is 65.2 %, and the SEM is 0.80 %; this is achieved through many runs of randomly selecting training and test sets for the prediction. Our study shows that two particular classifiers achieve the best accuracies: Random Forests and Logistic Regression. Our results reconfirm previous findings that females are more biased towards the left eyes of the stimuli.
{"title":"Predicting Gender via Eye Movements","authors":"Rishabh Vallabh Varsha Haria, Sahar Mahdie Klim Al Zaidawi, S. Maneth","doi":"10.48550/arXiv.2206.07442","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07442","url":null,"abstract":"In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it is around 4.1 %); this is achieved by varying the number of participants. And second, we are able to provide a mean accuracy with a very low standard error (SEM): our accuracy is 65.2 %, and the SEM is 0.80 %; this is achieved through many runs of randomly selecting training and test sets for the prediction. Our study shows that two particular classifiers achieve the best accuracies: Random Forests and Logistic Regression. Our results reconfirm previous findings that females are more biased towards the left eyes of the stimuli.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128658315","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 : 2022-06-11DOI: 10.48550/arXiv.2206.05629
Han Wang, Beril Bulat, Stephen Fujimoto, Seth Frey
Developing a strong community requires empowered leadership capable of overcoming governance challenges. New online platforms have given users opportunities to practice governance through content moderation roles. The over 2.8 million"subreddit"communities on Reddit are governed by hundreds of thousands of volunteer moderators, many of whom have no training or prior experience in a governing role. While moderators often devote daily time to community maintenance and cope with the emotional effects of hate comments or disturbing content, Reddit provides no compensation for this position. Thus, moderators' internal motivations and desire to continue filling this role is critical for their community. Drawing upon the relationship between governance procedures and internalized motivation, we investigate how the processes through which subreddit moderators generate community rules increase moderators' motivation through the meeting of social-psychological needs: Procedural Justice and Self Determination, and Self-Other Merging. Preliminary analysis of survey data from 620 moderators across Reddit shows a correlation between moderators' administrative behaviors and the social-psychological needs underpinning their motivations. Understanding these relationships will allow us to empower moderators to build engaging and cooperative online communities.
{"title":"Governing for Free: Rule Process Effects on Reddit Moderator Motivations","authors":"Han Wang, Beril Bulat, Stephen Fujimoto, Seth Frey","doi":"10.48550/arXiv.2206.05629","DOIUrl":"https://doi.org/10.48550/arXiv.2206.05629","url":null,"abstract":"Developing a strong community requires empowered leadership capable of overcoming governance challenges. New online platforms have given users opportunities to practice governance through content moderation roles. The over 2.8 million\"subreddit\"communities on Reddit are governed by hundreds of thousands of volunteer moderators, many of whom have no training or prior experience in a governing role. While moderators often devote daily time to community maintenance and cope with the emotional effects of hate comments or disturbing content, Reddit provides no compensation for this position. Thus, moderators' internal motivations and desire to continue filling this role is critical for their community. Drawing upon the relationship between governance procedures and internalized motivation, we investigate how the processes through which subreddit moderators generate community rules increase moderators' motivation through the meeting of social-psychological needs: Procedural Justice and Self Determination, and Self-Other Merging. Preliminary analysis of survey data from 620 moderators across Reddit shows a correlation between moderators' administrative behaviors and the social-psychological needs underpinning their motivations. Understanding these relationships will allow us to empower moderators to build engaging and cooperative online communities.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123295806","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 : 2022-06-10DOI: 10.1007/978-3-031-05643-7_11
Siyu Liu, Catherine Lu, Sharifa Alghowinem, Lea Gotoh, C. Breazeal, Hae Won Park
{"title":"Explainable AI for Suicide Risk Assessment Using Eye Activities and Head Gestures","authors":"Siyu Liu, Catherine Lu, Sharifa Alghowinem, Lea Gotoh, C. Breazeal, Hae Won Park","doi":"10.1007/978-3-031-05643-7_11","DOIUrl":"https://doi.org/10.1007/978-3-031-05643-7_11","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130343385","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 : 2022-05-28DOI: 10.1007/978-3-031-18158-0_37
Antonios Saravanos, Olivia Verni, Ian Moore, Sall Aboubacar, Jen Arriaza, Sabrina Jivani, Audrey Bennett, Siqi Li, Dongnanzi Zheng, Stavros Zervoudakis
{"title":"Investigating End-user Acceptance of Last-mile Delivery by Autonomous Vehicles in the United States","authors":"Antonios Saravanos, Olivia Verni, Ian Moore, Sall Aboubacar, Jen Arriaza, Sabrina Jivani, Audrey Bennett, Siqi Li, Dongnanzi Zheng, Stavros Zervoudakis","doi":"10.1007/978-3-031-18158-0_37","DOIUrl":"https://doi.org/10.1007/978-3-031-18158-0_37","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134298914","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 : 2022-04-29DOI: 10.48550/arXiv.2204.13943
K. Schulz, Jens Rauenbusch, Jan Fillies, Lisa Rutenburg, Dimitrios Karvelas, G. Rehm
The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of credibility assessment [18]. If large parts of the population are expected to act responsibly during a pandemic, they need information that can be trusted [20]. In that context, we model the credibility of texts using 25 linguistic phenomena, such as spelling, sentiment and lexical diversity. We integrate these measures in a graphical interface and present two empirical studies to evaluate its usability for credibility assessment on COVID-19 news. Raw data for the studies, including all questions and responses, has been made available to the public using an open license: https://github.com/konstantinschulz/credible-covid-ux. The user interface prominently features three sub-scores and an aggregation for a quick overview. Besides, metadata about the concept, authorship and infrastructure of the underlying algorithm is provided explicitly. Our working definition of credibility is operationalized through the terms of trustworthiness, understandability, transparency, and relevance. Each of them builds on well-established scientific notions [41, 65, 68] and is explained orally or through Likert scales. In a moderated qualitative interview with six participants, we introduce information transparency for news about COVID-19 as the general goal of a prototypical platform, accessible through an interface in the form of a wireframe [43]. The participants' answers are transcribed in excerpts. Then, we triangulate inductive and deductive coding methods [19] to analyze their content. As a result, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. In a subsequent quantitative online survey, we present a questionnaire with wireframes to 50 crowdworkers. The question formats include Likert scales, multiple choice and open-ended types. This way, we aim to strike a balance between the known strengths and weaknesses of open vs. closed questions [11]. The answers reveal a conflict between transparency and conciseness in the interface design: Users tend to ask for more information, but do not necessarily make explicit use of it when given. This discrepancy is influenced by capacity constraints of the human working memory [38]. Moreover, a perceived hierarchy of metadata becomes apparent: the authorship of a news text is more important than the authorship of the algorithm used to assess its credibility. From the first to the second study, we notice an improved usability of the aggregated credibility score's scale. That change is due to the conceptual introduction before seeing the actual interface, as well as the simplified binary indicators with direct visual support. Sub-scores need to be handled similarly if they are supposed to contribute meaningfully to the overall credibility assessment. By integrating detailed information about the employed algorithm, we are able to dissipate the users' doubts about its an
{"title":"User Experience Design for Automatic Credibility Assessment of News Content About COVID-19","authors":"K. Schulz, Jens Rauenbusch, Jan Fillies, Lisa Rutenburg, Dimitrios Karvelas, G. Rehm","doi":"10.48550/arXiv.2204.13943","DOIUrl":"https://doi.org/10.48550/arXiv.2204.13943","url":null,"abstract":"The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of credibility assessment [18]. If large parts of the population are expected to act responsibly during a pandemic, they need information that can be trusted [20]. In that context, we model the credibility of texts using 25 linguistic phenomena, such as spelling, sentiment and lexical diversity. We integrate these measures in a graphical interface and present two empirical studies to evaluate its usability for credibility assessment on COVID-19 news. Raw data for the studies, including all questions and responses, has been made available to the public using an open license: https://github.com/konstantinschulz/credible-covid-ux. The user interface prominently features three sub-scores and an aggregation for a quick overview. Besides, metadata about the concept, authorship and infrastructure of the underlying algorithm is provided explicitly. Our working definition of credibility is operationalized through the terms of trustworthiness, understandability, transparency, and relevance. Each of them builds on well-established scientific notions [41, 65, 68] and is explained orally or through Likert scales. In a moderated qualitative interview with six participants, we introduce information transparency for news about COVID-19 as the general goal of a prototypical platform, accessible through an interface in the form of a wireframe [43]. The participants' answers are transcribed in excerpts. Then, we triangulate inductive and deductive coding methods [19] to analyze their content. As a result, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. In a subsequent quantitative online survey, we present a questionnaire with wireframes to 50 crowdworkers. The question formats include Likert scales, multiple choice and open-ended types. This way, we aim to strike a balance between the known strengths and weaknesses of open vs. closed questions [11]. The answers reveal a conflict between transparency and conciseness in the interface design: Users tend to ask for more information, but do not necessarily make explicit use of it when given. This discrepancy is influenced by capacity constraints of the human working memory [38]. Moreover, a perceived hierarchy of metadata becomes apparent: the authorship of a news text is more important than the authorship of the algorithm used to assess its credibility. From the first to the second study, we notice an improved usability of the aggregated credibility score's scale. That change is due to the conceptual introduction before seeing the actual interface, as well as the simplified binary indicators with direct visual support. Sub-scores need to be handled similarly if they are supposed to contribute meaningfully to the overall credibility assessment. By integrating detailed information about the employed algorithm, we are able to dissipate the users' doubts about its an","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129039093","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 : 2022-03-25DOI: 10.48550/arXiv.2203.13592
Hange Wang, Haoran Xie, K. Miyata
Drawing eyeliner is not an easy task for whom lacks experience in eye makeup. Everyone has a unique pair of eyes, so they need to draw eyeliner in a style that suits their eyes. We proposed ILoveEye, an interactive system that supports eye-makeup novices to draw natural and suitable eyeliner. The proposed system analyzes the shape of the user's eyes and classifies the eye types from camera frame. The system can recommend the eyeliner style to the user based on the designed recommendation rules. Then, the system can generate the original patterns corresponding to the eyeliner style, and the user can draw the eyeliner while observing the real-time makeup guidance. The user evaluation experiments are conducted to verify that the proposed ILoveEye system can help some users to draw reasonable eyeliner based on the specific eye shapes and improve their eye makeup skills.
{"title":"ILoveEye: Eyeliner Makeup Guidance System with Eye Shape Features","authors":"Hange Wang, Haoran Xie, K. Miyata","doi":"10.48550/arXiv.2203.13592","DOIUrl":"https://doi.org/10.48550/arXiv.2203.13592","url":null,"abstract":"Drawing eyeliner is not an easy task for whom lacks experience in eye makeup. Everyone has a unique pair of eyes, so they need to draw eyeliner in a style that suits their eyes. We proposed ILoveEye, an interactive system that supports eye-makeup novices to draw natural and suitable eyeliner. The proposed system analyzes the shape of the user's eyes and classifies the eye types from camera frame. The system can recommend the eyeliner style to the user based on the designed recommendation rules. Then, the system can generate the original patterns corresponding to the eyeliner style, and the user can draw the eyeliner while observing the real-time makeup guidance. The user evaluation experiments are conducted to verify that the proposed ILoveEye system can help some users to draw reasonable eyeliner based on the specific eye shapes and improve their eye makeup skills.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131650642","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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