Pub Date : 2023-10-11DOI: 10.1007/978-3-031-35891-3_13
T. Decker, Ralf Gross, Alexander Koebler, Michael Lebacher, Ronald Schnitzer, Stefan H. Weber
{"title":"The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research","authors":"T. Decker, Ralf Gross, Alexander Koebler, Michael Lebacher, Ronald Schnitzer, Stefan H. Weber","doi":"10.1007/978-3-031-35891-3_13","DOIUrl":"https://doi.org/10.1007/978-3-031-35891-3_13","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"3 1","pages":"184-208"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320340","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 : 2023-07-23DOI: 10.1007/978-3-031-35894-4_13
Anna Stock, Stephan Schlögl, Aleksander Groth
{"title":"Tell Me, What Are You Most Afraid Of? Exploring the Effects of Agent Representation on Information Disclosure in Human-Chatbot Interaction","authors":"Anna Stock, Stephan Schlögl, Aleksander Groth","doi":"10.1007/978-3-031-35894-4_13","DOIUrl":"https://doi.org/10.1007/978-3-031-35894-4_13","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134450271","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 : 2023-04-08DOI: 10.1007/978-3-031-35634-6_2
Corrie Green, Yang Jiang, John Isaacs
{"title":"Modular 3D Interface Design for Accessible VR Applications","authors":"Corrie Green, Yang Jiang, John Isaacs","doi":"10.1007/978-3-031-35634-6_2","DOIUrl":"https://doi.org/10.1007/978-3-031-35634-6_2","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124130619","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 : 2023-04-04DOI: 10.48550/arXiv.2304.06039
E. Oikonomaki, Dimitris Belivanis
In an era of knowledge-based economy, commercialized research and globalized competition for talent, the creation of innovation ecosystems and innovation networks is at the forefront of efforts of cities. In this context, public authorities, private organizations, and academics respond to the question of the most promising indicators that can predict innovation with various innovation scoreboards. The current paper aims at increasing the understanding of the existing indicators and complementing the various innovation assessment toolkits, using large datasets from non-traditional sources. The success of both top down implemented innovation districts and community-level innovation ecosystems is complex and has not been well examined. Yet, limited data shed light on the association between indicators and innovation performance at the neighborhood level. For this purpose, the city of Boston has been selected as a case study to reveal the importance of its neighborhood's different characteristics in achieving high innovation performance. The study uses a large geographically distributed dataset across Boston's 35 zip code areas, which contains various business, entrepreneurial-specific, socio-economic data and other types of data that can reveal contextual urban dimensions. Furthermore, in order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations. The outcomes of this analysis aim to introduce a 'Neighborhood Innovation Index' that will generate new planning models for higher innovation performance, which can be easily applied in other cases. By publishing this large-scale dataset of urban informatics, the goal is to contribute to the innovation discourse and enable a new theoretical framework that identifies the linkages among cities' socio-economic characteristics and innovation performance.
{"title":"A new perspective on the prediction of the innovation performance: A data driven methodology to identify innovation indicators through a comparative study of Boston's neighborhoods","authors":"E. Oikonomaki, Dimitris Belivanis","doi":"10.48550/arXiv.2304.06039","DOIUrl":"https://doi.org/10.48550/arXiv.2304.06039","url":null,"abstract":"In an era of knowledge-based economy, commercialized research and globalized competition for talent, the creation of innovation ecosystems and innovation networks is at the forefront of efforts of cities. In this context, public authorities, private organizations, and academics respond to the question of the most promising indicators that can predict innovation with various innovation scoreboards. The current paper aims at increasing the understanding of the existing indicators and complementing the various innovation assessment toolkits, using large datasets from non-traditional sources. The success of both top down implemented innovation districts and community-level innovation ecosystems is complex and has not been well examined. Yet, limited data shed light on the association between indicators and innovation performance at the neighborhood level. For this purpose, the city of Boston has been selected as a case study to reveal the importance of its neighborhood's different characteristics in achieving high innovation performance. The study uses a large geographically distributed dataset across Boston's 35 zip code areas, which contains various business, entrepreneurial-specific, socio-economic data and other types of data that can reveal contextual urban dimensions. Furthermore, in order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations. The outcomes of this analysis aim to introduce a 'Neighborhood Innovation Index' that will generate new planning models for higher innovation performance, which can be easily applied in other cases. By publishing this large-scale dataset of urban informatics, the goal is to contribute to the innovation discourse and enable a new theoretical framework that identifies the linkages among cities' socio-economic characteristics and innovation performance.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132938404","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 : 2023-03-25DOI: 10.48550/arXiv.2304.06471
Eric Modesitt, Ruiqi Yang, Qi Liu
Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity.
{"title":"Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data","authors":"Eric Modesitt, Ruiqi Yang, Qi Liu","doi":"10.48550/arXiv.2304.06471","DOIUrl":"https://doi.org/10.48550/arXiv.2304.06471","url":null,"abstract":"Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128814005","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 : 2023-02-15DOI: 10.48550/arXiv.2302.09069
R. Fulbright, S. McGaha
When performing a task alone, humans achieve a certain level of performance. When humans are assisted by a tool or automation to perform the same task, performance is enhanced (augmented). Recently developed cognitive systems are able to perform cognitive processing at or above the level of a human in some domains. When humans work collaboratively with such cogs in a human/cog ensemble, we expect augmentation of cognitive processing to be evident and measurable. This paper shows the degree of cognitive augmentation depends on the nature of the information the cog contributes to the ensemble. Results of an experiment are reported showing conceptual information is the most effective type of information resulting in increases in cognitive accuracy, cognitive precision, and cognitive power.
{"title":"The Effect of Information Type on Human Cognitive Augmentation","authors":"R. Fulbright, S. McGaha","doi":"10.48550/arXiv.2302.09069","DOIUrl":"https://doi.org/10.48550/arXiv.2302.09069","url":null,"abstract":"When performing a task alone, humans achieve a certain level of performance. When humans are assisted by a tool or automation to perform the same task, performance is enhanced (augmented). Recently developed cognitive systems are able to perform cognitive processing at or above the level of a human in some domains. When humans work collaboratively with such cogs in a human/cog ensemble, we expect augmentation of cognitive processing to be evident and measurable. This paper shows the degree of cognitive augmentation depends on the nature of the information the cog contributes to the ensemble. Results of an experiment are reported showing conceptual information is the most effective type of information resulting in increases in cognitive accuracy, cognitive precision, and cognitive power.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131253787","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 : 2023-02-13DOI: 10.1007/978-3-031-35017-7_9
V. Kasatskii, A. Serheyuk, A. Serova, Sergey Titov, T. Bryksin
{"title":"The Effect of Perceptual Load on Performance within IDE in People with ADHD Symptoms","authors":"V. Kasatskii, A. Serheyuk, A. Serova, Sergey Titov, T. Bryksin","doi":"10.1007/978-3-031-35017-7_9","DOIUrl":"https://doi.org/10.1007/978-3-031-35017-7_9","url":null,"abstract":"","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116318947","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 : 2023-02-13DOI: 10.48550/arXiv.2302.06304
T. Catarci, Barbara Polidori, Daniel Raffini, P. Velardi
It has been observed in many studies that female students in general are unwilling to undertake a course of study in ICT. Recent literature has also pointed out that undermining the prejudices of girls with respect to these disciplines is very difficult in adolescence, suggesting that, to be effective, awareness programs on computer disciplines should be offered in pre-school or lower school age. On the other hand, even assuming that large-scale computer literacy programs can be immediately activated in lower schools and kindergartens, we can't wait for>15-20 years before we can appreciate the effectiveness of these programs. The scarcity of women in ICT has a tangible negative impact on countries' technological innovation, which requires immediate action. In this paper, we describe a strategy, and the details of a number of programs coordinated by the Engineering and Computer Science Departments at Sapienza University, to make high school girl students aware of the importance of new technologies and ICT. In addition to describing the theoretical approach, the paper offers some project examples.
{"title":"A Greed(y) Training Strategy to Attract High School Girls to Undertake Studies in ICT","authors":"T. Catarci, Barbara Polidori, Daniel Raffini, P. Velardi","doi":"10.48550/arXiv.2302.06304","DOIUrl":"https://doi.org/10.48550/arXiv.2302.06304","url":null,"abstract":"It has been observed in many studies that female students in general are unwilling to undertake a course of study in ICT. Recent literature has also pointed out that undermining the prejudices of girls with respect to these disciplines is very difficult in adolescence, suggesting that, to be effective, awareness programs on computer disciplines should be offered in pre-school or lower school age. On the other hand, even assuming that large-scale computer literacy programs can be immediately activated in lower schools and kindergartens, we can't wait for>15-20 years before we can appreciate the effectiveness of these programs. The scarcity of women in ICT has a tangible negative impact on countries' technological innovation, which requires immediate action. In this paper, we describe a strategy, and the details of a number of programs coordinated by the Engineering and Computer Science Departments at Sapienza University, to make high school girl students aware of the importance of new technologies and ICT. In addition to describing the theoretical approach, the paper offers some project examples.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132882396","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 : 2023-02-11DOI: 10.48550/arXiv.2302.05623
Reza Hadi Mogavi, Chao Deng, J. Hoffman, E. Haq, Sujit Gujar, A. Bucchiarone, Pan Hui
In recent years, the gamification research community has widely and frequently questioned the effectiveness of one-size-fits-all gamification schemes. In consequence, personalization seems to be an important part of any successful gamification design. Personalization can be improved by understanding user behavior and Hexad player/user type. This paper comes with an original research idea: It investigates whether users' game-related data (collected via various gamer-archetype surveys) can be used to predict their behavioral characteristics and Hexad user types in non-game (but gamified) contexts. The affinity that exists between the concepts of gamification and gaming provided us with the impetus for running this exploratory research. We conducted an initial survey study with 67 Stack Exchange users (as a case study). We discovered that users' gameplay information could reveal valuable and helpful information about their behavioral characteristics and Hexad user types in a non-gaming (but gamified) environment. The results of testing three gamer archetypes (i.e., Bartle, Big Five, and BrainHex) show that they can all help predict users' most dominant Stack Exchange behavioral characteristics and Hexad user type better than a random labeler's baseline. That said, of all the gamer archetypes analyzed in this paper, BrainHex performs the best. In the end, we introduce a research agenda for future work.
近年来，游戏化研究界广泛且频繁地质疑一刀切的游戏化方案的有效性。因此，个性化似乎是任何成功的游戏化设计的重要组成部分。个性化可以通过理解用户行为和Hexad玩家/用户类型而得到改善。这篇论文有一个原创的研究理念:它调查了用户的游戏相关数据(通过各种玩家原型调查收集)是否可以用于预测他们在非游戏(但游戏化)环境中的行为特征和Hexad用户类型。游戏化和游戏概念之间存在的亲和力为我们提供了进行这项探索性研究的动力。我们对67个Stack Exchange用户进行了初步调查研究(作为案例研究)。我们发现，在非游戏(但游戏化)环境中，用户的玩法信息可以揭示有关其行为特征和Hexad用户类型的有价值且有用的信息。测试三种玩家原型(即Bartle, Big Five和BrainHex)的结果表明，它们都可以帮助预测用户最主要的Stack Exchange行为特征和Hexad用户类型，而不是随机标记者的基线。也就是说，在本文分析的所有玩家原型中，BrainHex表现最好。最后，提出了今后工作的研究方向。
{"title":"Your Favorite Gameplay Speaks Volumes about You: Predicting User Behavior and Hexad Type","authors":"Reza Hadi Mogavi, Chao Deng, J. Hoffman, E. Haq, Sujit Gujar, A. Bucchiarone, Pan Hui","doi":"10.48550/arXiv.2302.05623","DOIUrl":"https://doi.org/10.48550/arXiv.2302.05623","url":null,"abstract":"In recent years, the gamification research community has widely and frequently questioned the effectiveness of one-size-fits-all gamification schemes. In consequence, personalization seems to be an important part of any successful gamification design. Personalization can be improved by understanding user behavior and Hexad player/user type. This paper comes with an original research idea: It investigates whether users' game-related data (collected via various gamer-archetype surveys) can be used to predict their behavioral characteristics and Hexad user types in non-game (but gamified) contexts. The affinity that exists between the concepts of gamification and gaming provided us with the impetus for running this exploratory research. We conducted an initial survey study with 67 Stack Exchange users (as a case study). We discovered that users' gameplay information could reveal valuable and helpful information about their behavioral characteristics and Hexad user types in a non-gaming (but gamified) environment. The results of testing three gamer archetypes (i.e., Bartle, Big Five, and BrainHex) show that they can all help predict users' most dominant Stack Exchange behavioral characteristics and Hexad user type better than a random labeler's baseline. That said, of all the gamer archetypes analyzed in this paper, BrainHex performs the best. In the end, we introduce a research agenda for future work.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114819145","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 : 2023-02-08DOI: 10.48550/arXiv.2302.04335
M. Khalil, Erkan Er
The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper.
{"title":"Will ChatGPT get you caught? Rethinking of Plagiarism Detection","authors":"M. Khalil, Erkan Er","doi":"10.48550/arXiv.2302.04335","DOIUrl":"https://doi.org/10.48550/arXiv.2302.04335","url":null,"abstract":"The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper.","PeriodicalId":129626,"journal":{"name":"Interacción","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514192","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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