Pub Date : 2022-03-23DOI: 10.48550/arXiv.2203.12342
A. Knoben, M. Alimardani, A. Saghafi, A. K. Amiri
. Conceptual models visually represent entities and relationships between them in an information system. Effective conceptual models should be simple while communicating sufficient information. This trade-off between model complexity and clarity is crucial to prevent failure of information system development. Past studies have found that more expressive models lead to higher performance on tasks measuring a user’s deep understanding of the model and attributed this to lower experience of cognitive workload associated with these models. This study examined this hypothesis by measuring users’ EEG brain activity while they completed a task with different conceptual models. 30 participants were divided into two groups: One group used a low ontologically expressive model (LOEM), and the other group used a high ontologically expressive model (HOEM). Cognitive workload during the task was quantified using EEG Engagement Index, which is a ratio of brain activity power in beta as opposed to the sum of alpha and theta frequency bands. No significant difference in cognitive workload was found between the LOEM and HOEM groups indicating equal amounts of cognitive processing required for understanding of both models. The main contribution of this study is the introduction of neurophysiological measures as an objective quantification of cognitive workload in the field of conceptual modeling and information systems.
{"title":"Cognitive Workload Associated with Different Conceptual Modeling Approaches in Information Systems","authors":"A. Knoben, M. Alimardani, A. Saghafi, A. K. Amiri","doi":"10.48550/arXiv.2203.12342","DOIUrl":"https://doi.org/10.48550/arXiv.2203.12342","url":null,"abstract":". Conceptual models visually represent entities and relationships between them in an information system. Effective conceptual models should be simple while communicating sufficient information. This trade-off between model complexity and clarity is crucial to prevent failure of information system development. Past studies have found that more expressive models lead to higher performance on tasks measuring a user’s deep understanding of the model and attributed this to lower experience of cognitive workload associated with these models. This study examined this hypothesis by measuring users’ EEG brain activity while they completed a task with different conceptual models. 30 participants were divided into two groups: One group used a low ontologically expressive model (LOEM), and the other group used a high ontologically expressive model (HOEM). Cognitive workload during the task was quantified using EEG Engagement Index, which is a ratio of brain activity power in beta as opposed to the sum of alpha and theta frequency bands. No significant difference in cognitive workload was found between the LOEM and HOEM groups indicating equal amounts of cognitive processing required for understanding of both models. The main contribution of this study is the introduction of neurophysiological measures as an objective quantification of cognitive workload in the field of conceptual modeling and information systems.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114991114","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 : 2022-02-23DOI: 10.1007/978-3-031-06018-2_22
A. Gutiérrez, V. Guda, S. Mugisha, C. Chevallereau, D. Chablat
{"title":"Trajectory planning in Dynamics Environment : Application for Haptic Perception in Safe HumanRobot Interaction","authors":"A. Gutiérrez, V. Guda, S. Mugisha, C. Chevallereau, D. Chablat","doi":"10.1007/978-3-031-06018-2_22","DOIUrl":"https://doi.org/10.1007/978-3-031-06018-2_22","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124663973","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}
By nearly every metric, the status quo of information security is not working. The interaction matrix of attacker-defender dynamics strongly favors the attacker who only needs to be lucky once. We argue that employing social engineering active defense (SEAD) will be more effective to countering malicious actors than maintaining the traditional passive defensive strategy. The Offensive Countermeasures (OCM) approach to defense advocates for three categories of countermeasures: annoyance, attribution, and attack. Annoyance aims to waste the attacker’s time and resources with the objective of not only deterrence but also to increase the probability of detection and attribution. Attribution attempts to identify who is launching the attack. Gathering as much threat intelligence on who the attacker is, provides the best possible defense against future attacks. Finally, attack involves running code on the attacker’s system for the purpose of deterrence and attribution. In this work, we advocate for utilizing similar approaches to deny, degrade, and de-anonymize malicious actors by using social engineering tools, tactics, and procedures against the attackers. Rather than fearing the threats posed by synthetic media, cyber defenders should embrace these capabilities by turning these against criminals. Future research should explore ways to implement synthetic media and automated SEAD methods to degrade the capabilities of online malicious actors.
{"title":"Planting a Poison SEAD: Using Social Engineering Active Defense (SEAD) to Counter Cybercriminals","authors":"Matthew Canham, Juliet Ruby Tuthill","doi":"10.31219/osf.io/6xj93","DOIUrl":"https://doi.org/10.31219/osf.io/6xj93","url":null,"abstract":"By nearly every metric, the status quo of information security is not working. The interaction matrix of attacker-defender dynamics strongly favors the attacker who only needs to be lucky once. We argue that employing social engineering active defense (SEAD) will be more effective to countering malicious actors than maintaining the traditional passive defensive strategy. The Offensive Countermeasures (OCM) approach to defense advocates for three categories of countermeasures: annoyance, attribution, and attack. Annoyance aims to waste the attacker’s time and resources with the objective of not only deterrence but also to increase the probability of detection and attribution. Attribution attempts to identify who is launching the attack. Gathering as much threat intelligence on who the attacker is, provides the best possible defense against future attacks. Finally, attack involves running code on the attacker’s system for the purpose of deterrence and attribution. In this work, we advocate for utilizing similar approaches to deny, degrade, and de-anonymize malicious actors by using social engineering tools, tactics, and procedures against the attackers. Rather than fearing the threats posed by synthetic media, cyber defenders should embrace these capabilities by turning these against criminals. Future research should explore ways to implement synthetic media and automated SEAD methods to degrade the capabilities of online malicious actors.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533130","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 : 2022-01-26DOI: 10.1007/978-3-031-34732-0_24
D. Meredith
{"title":"Understanding and Compressing Music with Maximal Transformable Patterns","authors":"D. Meredith","doi":"10.1007/978-3-031-34732-0_24","DOIUrl":"https://doi.org/10.1007/978-3-031-34732-0_24","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314841","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}
Stefan Sütterlin, Torvald F. Ask, Sophia Mägerle, Sandra Glöckler, Leandra Wolf, Julian Schray, Alaya Chandi, Teodora Bursac, Ali Khodabakhsh, Benjamin J. Knox, Matthew Canham, R. Lugo
AI-generated “deep fakes” are becoming increasingly professional and can be expected to become an essential tool for cybercriminals conducting targeted and tailored social engineering attacks, as well as for others aiming for influencing public opinion in a more general sense. While the technological arms race is resulting in increasingly efficient forensic detection tools, these are unlikely to be in place and applied by common users on an everyday basis any time soon, especially if social engineering attacks are camouflaged as unsuspicious conversations. To date, most cybercriminals do not yet have the necessary resources, competencies or the required raw material featuring the target to produce perfect impersonifications. To raise awareness and efficiently train individuals in recognizing the most widespread deep fakes, the understanding of what may cause individual differences in the ability to recognize them can be central. Previous research suggested a close relationship between political attitudes and top-down perceptual and subsequent cognitive processing styles. In this study, we aimed to investigate the impact of political attitudes and agreement with the political message content on the individual’s deep fake recognition skills.In this study, 163 adults (72 females = 44.2%) judged a series of video clips with politicians’ statements across the political spectrum regarding their authenticity and their agreement with the message that was transported. Half of the presented videos were fabricated via lip-sync technology. In addition to the particular agreement to each statement made, more global political attitudes towards social and economic topics were assessed via the Social and Economic Conservatism Scale (SECS).Data analysis revealed robust negative associations between participants’ general and in particular social conservatism and their ability to recognize fabricated videos. This effect was pronounced where there was a specific agreement with the message content. Deep fakes watched on mobile phones and tablets were considerably less likely to be recognized as such compared to when watched on stationary computers.To the best of our knowledge, this study is the first to investigate and establish the association between political attitudes and interindividual differences in deep fake recognition. The study further supports very recently published research suggesting relationships between conservatism and perceived credibility of conspiracy theories and fake news in general. Implications for further research on psychological mechanisms underlying this effect are discussed.
{"title":"Individual Deep Fake Recognition Skills are Affected by Viewer's Political Orientation, Agreement with Content and Device Used","authors":"Stefan Sütterlin, Torvald F. Ask, Sophia Mägerle, Sandra Glöckler, Leandra Wolf, Julian Schray, Alaya Chandi, Teodora Bursac, Ali Khodabakhsh, Benjamin J. Knox, Matthew Canham, R. Lugo","doi":"10.31234/osf.io/hwujb","DOIUrl":"https://doi.org/10.31234/osf.io/hwujb","url":null,"abstract":"AI-generated “deep fakes” are becoming increasingly professional and can be expected to become an essential tool for cybercriminals conducting targeted and tailored social engineering attacks, as well as for others aiming for influencing public opinion in a more general sense. While the technological arms race is resulting in increasingly efficient forensic detection tools, these are unlikely to be in place and applied by common users on an everyday basis any time soon, especially if social engineering attacks are camouflaged as unsuspicious conversations. To date, most cybercriminals do not yet have the necessary resources, competencies or the required raw material featuring the target to produce perfect impersonifications. To raise awareness and efficiently train individuals in recognizing the most widespread deep fakes, the understanding of what may cause individual differences in the ability to recognize them can be central. Previous research suggested a close relationship between political attitudes and top-down perceptual and subsequent cognitive processing styles. In this study, we aimed to investigate the impact of political attitudes and agreement with the political message content on the individual’s deep fake recognition skills.In this study, 163 adults (72 females = 44.2%) judged a series of video clips with politicians’ statements across the political spectrum regarding their authenticity and their agreement with the message that was transported. Half of the presented videos were fabricated via lip-sync technology. In addition to the particular agreement to each statement made, more global political attitudes towards social and economic topics were assessed via the Social and Economic Conservatism Scale (SECS).Data analysis revealed robust negative associations between participants’ general and in particular social conservatism and their ability to recognize fabricated videos. This effect was pronounced where there was a specific agreement with the message content. Deep fakes watched on mobile phones and tablets were considerably less likely to be recognized as such compared to when watched on stationary computers.To the best of our knowledge, this study is the first to investigate and establish the association between political attitudes and interindividual differences in deep fake recognition. The study further supports very recently published research suggesting relationships between conservatism and perceived credibility of conspiracy theories and fake news in general. Implications for further research on psychological mechanisms underlying this effect are discussed.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131153840","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 : 2021-11-30DOI: 10.1007/978-3-031-05563-8_22
F. Kammüller, Chelsea Mira Alvarado
{"title":"Exploring rationality of self awareness in social networking for logical modeling of unintentional insiders","authors":"F. Kammüller, Chelsea Mira Alvarado","doi":"10.1007/978-3-031-05563-8_22","DOIUrl":"https://doi.org/10.1007/978-3-031-05563-8_22","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065759","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}
Lottery gambling is widely enjoyed by Canadians and is the most popular form of legal gambling. As such, discovering and analyzing patterns in lottery gambling data is an important but nontrivial task. In this work, three methods were presented to process and visualize it to the end user to allow for faster pattern discovery. A bubble graph was utilized for the comparative analysis of lottery sales per each neighbourhood of the city of Toronto, Canada. As well, a scatter plot was used to explore the relationship between different neighbourhoods, lottery game product, year, lottery ticket sales, and demographic information. Lastly, a line graph was deployed to compare the jackpot size and ticket sales over time. shinyJackpot is deployed at https://andrewcli.shinyapps.io/shinyJackpot/ for online use. The repository is available at https://github.com/andr3wli/shinyapps.
{"title":"shinyJackpot: Visualizing Lottery Gambling in a Large Canadian City","authors":"Andrew Li","doi":"10.31234/osf.io/ksqhb","DOIUrl":"https://doi.org/10.31234/osf.io/ksqhb","url":null,"abstract":"Lottery gambling is widely enjoyed by Canadians and is the most popular form of legal gambling. As such, discovering and analyzing patterns in lottery gambling data is an important but nontrivial task. In this work, three methods were presented to process and visualize it to the end user to allow for faster pattern discovery. A bubble graph was utilized for the comparative analysis of lottery sales per each neighbourhood of the city of Toronto, Canada. As well, a scatter plot was used to explore the relationship between different neighbourhoods, lottery game product, year, lottery ticket sales, and demographic information. Lastly, a line graph was deployed to compare the jackpot size and ticket sales over time. shinyJackpot is deployed at https://andrewcli.shinyapps.io/shinyJackpot/ for online use. The repository is available at https://github.com/andr3wli/shinyapps.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124326096","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 : 2021-07-24DOI: 10.1007/978-3-030-77074-7_14
Yufan Xie
{"title":"Shift in Computation - Tangible to Intangible","authors":"Yufan Xie","doi":"10.1007/978-3-030-77074-7_14","DOIUrl":"https://doi.org/10.1007/978-3-030-77074-7_14","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114996939","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 : 2021-07-24DOI: 10.1007/978-3-030-78108-8_2
Kyle Harrington, M. Craven, Max L. Wilson, A. Landowska
{"title":"Exploring User Opinion on the Benefits of Cognitive Games Through an Online Walkthrough and Interview","authors":"Kyle Harrington, M. Craven, Max L. Wilson, A. Landowska","doi":"10.1007/978-3-030-78108-8_2","DOIUrl":"https://doi.org/10.1007/978-3-030-78108-8_2","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764938","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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