Pub Date : 2023-01-21DOI: 10.1007/978-3-031-35894-4_17
Nikolos Gurney, D. Pynadath, Ning Wang
{"title":"My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs about Agents' Attributes","authors":"Nikolos Gurney, D. Pynadath, Ning Wang","doi":"10.1007/978-3-031-35894-4_17","DOIUrl":"https://doi.org/10.1007/978-3-031-35894-4_17","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125389314","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 : 2023-01-06DOI: 10.48550/arXiv.2301.02532
Elisavet Kozyri, Mariel Evelyn Markussen Ellingsen, Ragnhild Abel Grape, M. L. Jaccheri
In recent years, there has been considerable effort to promote gender balance in the academic environment of Computer Science (CS). However, there is still a gender gap at all CS academic levels: from students, to PhD candidates, to faculty members. This general trend is followed by the Department of Computer Science at UiT The Arctic University of Norway. To combat this trend within the CS environment at UiT, we embarked on structured discussions with students of our department. After analyzing the data collected from these discussions, we were able to identify action items that could mitigate the existing gender gap at our department. In particular, these discussions elucidated ways to achieve (i) a balanced flow of students into CS undergraduate program, (ii) a balanced CS study environment, and (iii) a balanced flow of graduates into higher levels of the CS academia (e.g., PhD program). This paper presents the results of the discussions and the subsequent recommendations that we made to the administration of the department. We also provide a road-map that other institutions could follow to organize similar events as part of their gender-balance action plan.
{"title":"Better Balance in Informatics: An Honest Discussion with Students","authors":"Elisavet Kozyri, Mariel Evelyn Markussen Ellingsen, Ragnhild Abel Grape, M. L. Jaccheri","doi":"10.48550/arXiv.2301.02532","DOIUrl":"https://doi.org/10.48550/arXiv.2301.02532","url":null,"abstract":"In recent years, there has been considerable effort to promote gender balance in the academic environment of Computer Science (CS). However, there is still a gender gap at all CS academic levels: from students, to PhD candidates, to faculty members. This general trend is followed by the Department of Computer Science at UiT The Arctic University of Norway. To combat this trend within the CS environment at UiT, we embarked on structured discussions with students of our department. After analyzing the data collected from these discussions, we were able to identify action items that could mitigate the existing gender gap at our department. In particular, these discussions elucidated ways to achieve (i) a balanced flow of students into CS undergraduate program, (ii) a balanced CS study environment, and (iii) a balanced flow of graduates into higher levels of the CS academia (e.g., PhD program). This paper presents the results of the discussions and the subsequent recommendations that we made to the administration of the department. We also provide a road-map that other institutions could follow to organize similar events as part of their gender-balance action plan.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124242996","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 : 2023-01-01DOI: 10.1007/978-3-031-35822-7_11
Zimeng Gao, Fei Xing, G. Peng
{"title":"Research on the Capability Maturity Model of Data Security in the Era of Digital Transformation","authors":"Zimeng Gao, Fei Xing, G. Peng","doi":"10.1007/978-3-031-35822-7_11","DOIUrl":"https://doi.org/10.1007/978-3-031-35822-7_11","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133166179","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}
R. Nakatsu, Manae Miyata, Hirotaka Kawata, N. Tosa, T. Kusumi
{"title":"Reflected Light vs. Transmitted Light: Do They Give Different Impressions to Users?","authors":"R. Nakatsu, Manae Miyata, Hirotaka Kawata, N. Tosa, T. Kusumi","doi":"10.36463/idw.2022.0690","DOIUrl":"https://doi.org/10.36463/idw.2022.0690","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114394091","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 : 2022-11-08DOI: 10.48550/arXiv.2211.03936
O. Deng, Q. Jin
This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement Learning (MARL) is proposed, adding a multi-head attention function to the A3C algorithm to enhance learning effectiveness. This approach simulates human behavior in certain scenarios through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning is employed to serve specific agents that learn from environment status and competitor behaviors, optimizing strategies to achieve better results. The simulation includes individual and group levels, displaying possible paths to forming competitive advantages. This modeling approach provides a means for further analysis of the evolutionary dynamics of human behavior, communities, and organizations in various socioeconomic issues.
{"title":"Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling","authors":"O. Deng, Q. Jin","doi":"10.48550/arXiv.2211.03936","DOIUrl":"https://doi.org/10.48550/arXiv.2211.03936","url":null,"abstract":"This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement Learning (MARL) is proposed, adding a multi-head attention function to the A3C algorithm to enhance learning effectiveness. This approach simulates human behavior in certain scenarios through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning is employed to serve specific agents that learn from environment status and competitor behaviors, optimizing strategies to achieve better results. The simulation includes individual and group levels, displaying possible paths to forming competitive advantages. This modeling approach provides a means for further analysis of the evolutionary dynamics of human behavior, communities, and organizations in various socioeconomic issues.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128851152","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 : 2022-10-14DOI: 10.1007/978-3-031-17615-9_33
Minghuan Shou, Xueqi Bao, Jie Yu
{"title":"Predictions on Usefulness and Popularity of Online Reviews: Evidence from Mobile Phones for Older Adults","authors":"Minghuan Shou, Xueqi Bao, Jie Yu","doi":"10.1007/978-3-031-17615-9_33","DOIUrl":"https://doi.org/10.1007/978-3-031-17615-9_33","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126373713","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 : 2022-10-13DOI: 10.48550/arXiv.2210.07286
Arnab Sen Sharma, M. R. Amin, M. Fuad
Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers' limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate-level online classes on the necessity of an attention tracking tool to understand student engagement during an online class. This paper outlines the design of such a monitoring tool that automatically tracks the attentiveness of the whole class by tracking students' gazes on the screen and alerts the teacher when the attention score goes below a certain threshold. We assume the benefits are twofold; 1) teachers will be able to ascertain if the students are attentive or being engaged with the lecture contents and 2) the students will become more attentive in online classes because of this passive monitoring system. In this paper, we present the preliminary design and feasibility of using the proposed tool and discuss its applicability in augmenting online classes. Finally, we surveyed 31 students asking their opinion on the usability as well as the ethical and privacy concerns of using such a monitoring tool.
{"title":"Augmenting Online Classes with an Attention Tracking Tool May Improve Student Engagement","authors":"Arnab Sen Sharma, M. R. Amin, M. Fuad","doi":"10.48550/arXiv.2210.07286","DOIUrl":"https://doi.org/10.48550/arXiv.2210.07286","url":null,"abstract":"Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers' limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate-level online classes on the necessity of an attention tracking tool to understand student engagement during an online class. This paper outlines the design of such a monitoring tool that automatically tracks the attentiveness of the whole class by tracking students' gazes on the screen and alerts the teacher when the attention score goes below a certain threshold. We assume the benefits are twofold; 1) teachers will be able to ascertain if the students are attentive or being engaged with the lecture contents and 2) the students will become more attentive in online classes because of this passive monitoring system. In this paper, we present the preliminary design and feasibility of using the proposed tool and discuss its applicability in augmenting online classes. Finally, we surveyed 31 students asking their opinion on the usability as well as the ethical and privacy concerns of using such a monitoring tool.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133771857","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 : 2022-09-01DOI: 10.1007/978-3-031-35936-1_22
Qianwei Wu, Han Jiang, Wenyan Lu
{"title":"Exploring the Empowerment of Chinese Women's Discourse in Tik Tok","authors":"Qianwei Wu, Han Jiang, Wenyan Lu","doi":"10.1007/978-3-031-35936-1_22","DOIUrl":"https://doi.org/10.1007/978-3-031-35936-1_22","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122501351","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 : 2022-08-17DOI: 10.48550/arXiv.2208.08198
R. Adler, M. Klaes
. The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all kinds of software. In particular, software based on machine learning (ML) are not considered for the realization of safety-relevant behavior. This limits the introduction of suitable safety concepts for autonomous mobile robots and other autonomous machinery, which commonly depend on ML-based functions. We investigated this issue and the way safety standards define safety measures to be implemented against software faults. Functional safety standards use Safety Integrity Levels (SILs) to define which safety measures shall be implemented. They provide rules for determining the SIL and rules for selecting safety measures depending on the SIL. In this paper, we argue that this approach can hardly be adopted with respect to ML and other kinds of Artificial Intelligence (AI). Instead of simple rules for determining an SIL and applying related measures against faults, we propose the use of assurance cases to argue that the individually selected and applied measures are sufficient in the given case. To get a first rating regarding the feasibility and usefulness of our proposal, we presented and discussed it in a workshop with experts from industry, German statu-tory accident insurance companies, work safety and standardization commis-sions, and representatives from various national, European, and international working groups dealing with safety and AI. In this paper, we summarize the proposal and the workshop discussion. Moreover, we check to which extent our proposal is in line with the European AI Act proposal and current safety standardization initiatives addressing AI and Autonomous Systems.
{"title":"Assurance Cases as Foundation Stone for Auditing AI-enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project","authors":"R. Adler, M. Klaes","doi":"10.48550/arXiv.2208.08198","DOIUrl":"https://doi.org/10.48550/arXiv.2208.08198","url":null,"abstract":". The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all kinds of software. In particular, software based on machine learning (ML) are not considered for the realization of safety-relevant behavior. This limits the introduction of suitable safety concepts for autonomous mobile robots and other autonomous machinery, which commonly depend on ML-based functions. We investigated this issue and the way safety standards define safety measures to be implemented against software faults. Functional safety standards use Safety Integrity Levels (SILs) to define which safety measures shall be implemented. They provide rules for determining the SIL and rules for selecting safety measures depending on the SIL. In this paper, we argue that this approach can hardly be adopted with respect to ML and other kinds of Artificial Intelligence (AI). Instead of simple rules for determining an SIL and applying related measures against faults, we propose the use of assurance cases to argue that the individually selected and applied measures are sufficient in the given case. To get a first rating regarding the feasibility and usefulness of our proposal, we presented and discussed it in a workshop with experts from industry, German statu-tory accident insurance companies, work safety and standardization commis-sions, and representatives from various national, European, and international working groups dealing with safety and AI. In this paper, we summarize the proposal and the workshop discussion. Moreover, we check to which extent our proposal is in line with the European AI Act proposal and current safety standardization initiatives addressing AI and Autonomous Systems.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703300","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 : 2022-07-31DOI: 10.48550/arXiv.2208.00465
Brian Xiang, Abdelrahman Abdelmonsef
The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended interference of brain signals from multiple sources. Increasing the robustness of machine learning models in predicting eye-tracking position from EEG data is therefore integral for both research and consumer use. In medical research, the usage of more complicated data collection methods to test for simpler tasks has been explored to address this very issue. In this study, we propose a fine-grain data approach for EEG-ET data collection in order to create more robust benchmarking. We train machine learning models utilizing both coarse-grain and fine-grain data and compare their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to differences in distributional data. We apply a covariate distributional shift to test for this susceptibility. Results showed that models trained on fine-grain, vector-based data were less susceptible to distributional shifts than models trained on coarse-grain, binary-classified data.
{"title":"Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift","authors":"Brian Xiang, Abdelrahman Abdelmonsef","doi":"10.48550/arXiv.2208.00465","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00465","url":null,"abstract":"The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended interference of brain signals from multiple sources. Increasing the robustness of machine learning models in predicting eye-tracking position from EEG data is therefore integral for both research and consumer use. In medical research, the usage of more complicated data collection methods to test for simpler tasks has been explored to address this very issue. In this study, we propose a fine-grain data approach for EEG-ET data collection in order to create more robust benchmarking. We train machine learning models utilizing both coarse-grain and fine-grain data and compare their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to differences in distributional data. We apply a covariate distributional shift to test for this susceptibility. Results showed that models trained on fine-grain, vector-based data were less susceptible to distributional shifts than models trained on coarse-grain, binary-classified data.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125950433","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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