In this paper we present a semantics-aware recommendation strategy that uses graph embedding techniques to learn a vector space reresentation of the items to be recommended. Such a representation relies on the tripartite graph which connects users, items and entities gathered from DBpedia, thus it encodes both collaborative and content-based information. These embeddings are then used to feed with positive and negative examples (the items the user liked and those she did not like) a classification model, which is finally exploited to classify new items as interesting or not interesting for the target user. In the experimental evaluation we evaluate the effectiveness of our method on varying of different graph embedding techniques and on several topologies of the graph. Results show that the embeddings learnt by combining collaborative data points with the information gathered from DBpedia led to the best results and also beat several state-of-the-art techniques.
{"title":"Embedding Knowledge Graphs for Semantics-aware Recommendations based on DBpedia","authors":"C. Musto, Pierpaolo Basile, G. Semeraro","doi":"10.1145/3314183.3324976","DOIUrl":"https://doi.org/10.1145/3314183.3324976","url":null,"abstract":"In this paper we present a semantics-aware recommendation strategy that uses graph embedding techniques to learn a vector space reresentation of the items to be recommended. Such a representation relies on the tripartite graph which connects users, items and entities gathered from DBpedia, thus it encodes both collaborative and content-based information. These embeddings are then used to feed with positive and negative examples (the items the user liked and those she did not like) a classification model, which is finally exploited to classify new items as interesting or not interesting for the target user. In the experimental evaluation we evaluate the effectiveness of our method on varying of different graph embedding techniques and on several topologies of the graph. Results show that the embeddings learnt by combining collaborative data points with the information gathered from DBpedia led to the best results and also beat several state-of-the-art techniques.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124086247","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}
Passwords are still a widespread authentication mechanism that, despite the efforts of the cybersecurity community to educate people, are often predictable. Therefore, there is a need for defenders, e.g. cybersecurity/IT administrators, to periodically assess the users' passwords in their organization, improve their awareness on the security level and take measures to improve the situation. Password cracking can assist in the evaluation of the strength of passwords and a variety of tools exist to execute it. The challenge with this is that it is a time-consuming process and it needs to be optimized to detect weak passwords within a specific evaluation timeframe. To optimize the process, knowledge in the area and appropriate tools are required. However, even though a lot of research is performed in this area, the knowledge and tools are scarce, challenging defenders' tasks. Therefore, the need arises to promote the design of advanced tools, integrating existing user knowledge and creating powerful toolkits. This work presents the design of UPAT (Ultimate Password Awareness Toolkit), which specifies essential features to optimize the password cracking process. The evaluation results are encouraging as to the tool's effectiveness and users' satisfaction, demonstrating the importance of designing next generation password cracking toolkits to enhance the security of communication and information systems.
{"title":"Towards Designing Advanced Password Cracking Toolkits: Optimizing the Password Cracking Process","authors":"P. Jourdan, Eliana Stavrou","doi":"10.1145/3314183.3324967","DOIUrl":"https://doi.org/10.1145/3314183.3324967","url":null,"abstract":"Passwords are still a widespread authentication mechanism that, despite the efforts of the cybersecurity community to educate people, are often predictable. Therefore, there is a need for defenders, e.g. cybersecurity/IT administrators, to periodically assess the users' passwords in their organization, improve their awareness on the security level and take measures to improve the situation. Password cracking can assist in the evaluation of the strength of passwords and a variety of tools exist to execute it. The challenge with this is that it is a time-consuming process and it needs to be optimized to detect weak passwords within a specific evaluation timeframe. To optimize the process, knowledge in the area and appropriate tools are required. However, even though a lot of research is performed in this area, the knowledge and tools are scarce, challenging defenders' tasks. Therefore, the need arises to promote the design of advanced tools, integrating existing user knowledge and creating powerful toolkits. This work presents the design of UPAT (Ultimate Password Awareness Toolkit), which specifies essential features to optimize the password cracking process. The evaluation results are encouraging as to the tool's effectiveness and users' satisfaction, demonstrating the importance of designing next generation password cracking toolkits to enhance the security of communication and information systems.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129108543","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}
Maria Esteller-Cucala, Vicenc Fernandez, Diego Villuendas
Online controlled experiments (also called A/B tests, bucket testing or randomized experiments) have become an habitual practice in numerous companies for measuring the impact of new features and changes deployed to softwares products. In theory, these experiments are one of the simplest methods to evaluate the potential effects that new features have on user's behavior. In practice, however, there are many pitfalls that can obscure the interpretation of results or induce invalid conclusions. There is, in the literature, no shortage of prior work on online controlled experiments addressing these pitfalls and conclusions misinterpretations, but the topic is not tackled considering the specific case of testing personalization features. In this paper, we present some of the experimentation pitfalls that are particularly important for personalization features. To better illustrate each pitfall, we include a combination of theoretical argumentation as well as examples from real company's experiments. While there is clearly value in evaluating personalized features by means of online controlled experiments, there are some pitfalls to bear in mind while testing. With this paper, we aim to increase the experimenters' awareness of leading to improved quality and reliability of the results.
{"title":"Experimentation Pitfalls to Avoid in A/B Testing for Online Personalization","authors":"Maria Esteller-Cucala, Vicenc Fernandez, Diego Villuendas","doi":"10.1145/3314183.3323853","DOIUrl":"https://doi.org/10.1145/3314183.3323853","url":null,"abstract":"Online controlled experiments (also called A/B tests, bucket testing or randomized experiments) have become an habitual practice in numerous companies for measuring the impact of new features and changes deployed to softwares products. In theory, these experiments are one of the simplest methods to evaluate the potential effects that new features have on user's behavior. In practice, however, there are many pitfalls that can obscure the interpretation of results or induce invalid conclusions. There is, in the literature, no shortage of prior work on online controlled experiments addressing these pitfalls and conclusions misinterpretations, but the topic is not tackled considering the specific case of testing personalization features. In this paper, we present some of the experimentation pitfalls that are particularly important for personalization features. To better illustrate each pitfall, we include a combination of theoretical argumentation as well as examples from real company's experiments. While there is clearly value in evaluating personalized features by means of online controlled experiments, there are some pitfalls to bear in mind while testing. With this paper, we aim to increase the experimenters' awareness of leading to improved quality and reliability of the results.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128199877","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}
In this paper we point out some relevant issues in relation to privacy when providing holistic recommendations. We emphasize that a holistic recommender should be fair, explainable and privacy-preserving to ensure the ethicality of the recommendation process. Further, we point out relevant research questions that should be addressed in the future, as well as propose some preliminary suggestions to face the emergent issues with reference to privacy in the recommendation domain.
{"title":"Privacy Issues in Holistic Recommendations","authors":"F. Cena, R. Pensa, A. Rapp","doi":"10.1145/3314183.3323461","DOIUrl":"https://doi.org/10.1145/3314183.3323461","url":null,"abstract":"In this paper we point out some relevant issues in relation to privacy when providing holistic recommendations. We emphasize that a holistic recommender should be fair, explainable and privacy-preserving to ensure the ethicality of the recommendation process. Further, we point out relevant research questions that should be addressed in the future, as well as propose some preliminary suggestions to face the emergent issues with reference to privacy in the recommendation domain.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126012286","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}
Jordan Barria-Pineda, Kamil Akhuseyinoglu, Peter Brusilovsky
Students might pursue different goals throughout their learning process. For example, they might be seeking new material to expand their current level of knowledge, repeating content of prior classes to prepare for an exam, or working on addressing their most recent misconceptions. Multiple potential goals require an adaptive e-learning system to recommend learning content appropriate for students' intent and to explain this recommendation in the context of this goal. In our prior work, we explored explainable recommendations for the most typical 'knowledge expansion goal". In this paper, we focus on students' immediate needs to remedy misunderstandings when they solve programming problems. We generate learning content recommendations to target the concepts with which students have struggled more recently. At the same time, we produce explanations for this recommendation goal in order to support students' understanding of why certain learning activities are recommended. The paper provides an overview of the design of this explainable educational recommender system and describes its ongoing evaluation
{"title":"Explaining Need-based Educational Recommendations Using Interactive Open Learner Models","authors":"Jordan Barria-Pineda, Kamil Akhuseyinoglu, Peter Brusilovsky","doi":"10.1145/3314183.3323463","DOIUrl":"https://doi.org/10.1145/3314183.3323463","url":null,"abstract":"Students might pursue different goals throughout their learning process. For example, they might be seeking new material to expand their current level of knowledge, repeating content of prior classes to prepare for an exam, or working on addressing their most recent misconceptions. Multiple potential goals require an adaptive e-learning system to recommend learning content appropriate for students' intent and to explain this recommendation in the context of this goal. In our prior work, we explored explainable recommendations for the most typical 'knowledge expansion goal\". In this paper, we focus on students' immediate needs to remedy misunderstandings when they solve programming problems. We generate learning content recommendations to target the concepts with which students have struggled more recently. At the same time, we produce explanations for this recommendation goal in order to support students' understanding of why certain learning activities are recommended. The paper provides an overview of the design of this explainable educational recommender system and describes its ongoing evaluation","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125193626","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}
As already pointed out by a constantly growing literature, explainability in recommender systems field is a key aspect to increase users' satisfaction. With the increase of user generated content, tags have proven to be highly relevant when it comes to describe either users or items. A number of strategies that rely on tags have been proposed, yet, many of these algorithms exploit the frequency of user-tags interactions to gain information. We argue that a pure frequentist description might lack of specificity to grasp user's peculiar tastes. Therefore, we propose a novel approach based on game theory that tries to find the best trade-off between generality and detailing. The identified user's description can be used to keep her in the loop and allows the user to have control over system's knowledge. Additionally, we propose a user interface that embeds the proposed user's description and it can be used by the user herself to guide her catalogue's exploration toward novel and serendipitous items.
{"title":"Tag-Based User Profiling: A Game Theoretic Approach","authors":"G. Faggioli, Mirko Polato, F. Aiolli","doi":"10.1145/3314183.3323462","DOIUrl":"https://doi.org/10.1145/3314183.3323462","url":null,"abstract":"As already pointed out by a constantly growing literature, explainability in recommender systems field is a key aspect to increase users' satisfaction. With the increase of user generated content, tags have proven to be highly relevant when it comes to describe either users or items. A number of strategies that rely on tags have been proposed, yet, many of these algorithms exploit the frequency of user-tags interactions to gain information. We argue that a pure frequentist description might lack of specificity to grasp user's peculiar tastes. Therefore, we propose a novel approach based on game theory that tries to find the best trade-off between generality and detailing. The identified user's description can be used to keep her in the loop and allows the user to have control over system's knowledge. Additionally, we propose a user interface that embeds the proposed user's description and it can be used by the user herself to guide her catalogue's exploration toward novel and serendipitous items.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121645203","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}
Recovering from Stroke can be a very long and stressful process. It may involve several months or years of exercise and other medical routines that could be both painful and uninteresting. Most people suffering from stroke tend to lose their basic motor functions and it may take a series of exercises to gain back their full manual dexterity. There is a need for a variety of interventions to make these exercises engaging and exciting, to ease the burden of stroke rehabilitation. This paper explores the possibility of using portable personal electroencephalogram (EEG) devices with persuasive games as a tool for stroke rehabilitation. It looks at the major limitations and strengths of using personal Brain-Computer Interfaces (BCIs) for stroke rehabilitation. The paper also presents the design and development of a Brain-Computer Interface persuasive game called Rock Evaders, that aims to motivate people recovering from stroke to carry out their rehabilitation exercises in an exciting and engaging way.
{"title":"Driving Persuasive Games with Personal EEG Devices: Strengths and Weaknesses","authors":"Chinenye Ndulue, Rita Orji","doi":"10.1145/3314183.3325008","DOIUrl":"https://doi.org/10.1145/3314183.3325008","url":null,"abstract":"Recovering from Stroke can be a very long and stressful process. It may involve several months or years of exercise and other medical routines that could be both painful and uninteresting. Most people suffering from stroke tend to lose their basic motor functions and it may take a series of exercises to gain back their full manual dexterity. There is a need for a variety of interventions to make these exercises engaging and exciting, to ease the burden of stroke rehabilitation. This paper explores the possibility of using portable personal electroencephalogram (EEG) devices with persuasive games as a tool for stroke rehabilitation. It looks at the major limitations and strengths of using personal Brain-Computer Interfaces (BCIs) for stroke rehabilitation. The paper also presents the design and development of a Brain-Computer Interface persuasive game called Rock Evaders, that aims to motivate people recovering from stroke to carry out their rehabilitation exercises in an exciting and engaging way.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"22 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120980044","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}
Vasilis Ververis, Marios Isaakidis, Valentin Weber, Benjamin Fabian
This paper studies the availability of apps and app stores across countries. Our research finds that users in specific countries do not have access to popular app stores due to local laws, financial reasons, or because countries are on a sanctions list that prohibit foreign businesses to operate within its jurisdiction. Furthermore, this paper presents a novel methodology for querying the public search engines and APIs of major app stores (Google Play Store, Apple App Store, Tencent MyApp Store) that is cross-verified by network measurements. This allows us to investigate which apps are available in which country. We primarily focused on the availability of VPN apps in Russia and China. Our results show that despite both countries having restrictive VPN laws, there are still many VPN apps available in Russia and only a handful in China. In addition, we have included findings of a global search for the availability of privacy-enhancing and other apps that are known to be censored. Finally, we observe that it is difficult to find out which apps have been removed or are unavailable on the examined app stores. As a consequence, we urge all app store providers to introduce app store transparency reports, which would include when apps were removed and for what reasons.
本文研究了各国应用程序和应用商店的可用性。我们的研究发现，某些国家的用户由于当地法律、经济原因或某些国家被列入禁止外国企业在其管辖范围内运营的制裁名单而无法访问热门应用商店。此外，本文提出了一种新的方法，用于查询主要应用商店(Google Play Store, Apple app Store, Tencent MyApp Store)的公共搜索引擎和api，该方法通过网络测量进行交叉验证。这使我们能够调查哪些应用程序在哪个国家可用。我们主要关注的是俄罗斯和中国VPN应用的可用性。我们的研究结果显示，尽管这两个国家都有严格的VPN法律，但俄罗斯仍然有许多VPN应用程序可用，而中国只有少数VPN应用程序可用。此外，我们还在全球范围内搜索了隐私增强和其他已知受到审查的应用程序的可用性。最后，我们观察到很难发现哪些应用已经被删除或在被检查的应用商店中不可用。因此，我们敦促所有应用商店供应商引入应用商店透明度报告，其中包括应用程序何时被删除以及出于何种原因。
{"title":"Shedding Light on Mobile App Store Censorship","authors":"Vasilis Ververis, Marios Isaakidis, Valentin Weber, Benjamin Fabian","doi":"10.1145/3314183.3324965","DOIUrl":"https://doi.org/10.1145/3314183.3324965","url":null,"abstract":"This paper studies the availability of apps and app stores across countries. Our research finds that users in specific countries do not have access to popular app stores due to local laws, financial reasons, or because countries are on a sanctions list that prohibit foreign businesses to operate within its jurisdiction. Furthermore, this paper presents a novel methodology for querying the public search engines and APIs of major app stores (Google Play Store, Apple App Store, Tencent MyApp Store) that is cross-verified by network measurements. This allows us to investigate which apps are available in which country. We primarily focused on the availability of VPN apps in Russia and China. Our results show that despite both countries having restrictive VPN laws, there are still many VPN apps available in Russia and only a handful in China. In addition, we have included findings of a global search for the availability of privacy-enhancing and other apps that are known to be censored. Finally, we observe that it is difficult to find out which apps have been removed or are unavailable on the examined app stores. As a consequence, we urge all app store providers to introduce app store transparency reports, which would include when apps were removed and for what reasons.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131231457","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}
Argyris Constantinides, C. Fidas, Marios Belk, A. Pitsillides
Recent works underpin the added value of considering users' past sociocultural experiences as a personalization factor for the image content used within graphical password schemes, since it has a positive impact on the security and memorability of the user-chosen passwords. This paper discusses the need for personalization of the image content used in graphical password schemes, as well as the initial steps towards the realization of an image content personalization framework that aims to achieve a better equilibrium between security and memorability. The paper also discusses emerging challenges related to the elicitation and maintenance of individual sociocultural-centered user models, the image content personalization mechanism and privacy considerations.
{"title":"On the Personalization of Image Content in Graphical Passwords based on Users' Sociocultural Experiences: New Challenges and Opportunities","authors":"Argyris Constantinides, C. Fidas, Marios Belk, A. Pitsillides","doi":"10.1145/3314183.3324966","DOIUrl":"https://doi.org/10.1145/3314183.3324966","url":null,"abstract":"Recent works underpin the added value of considering users' past sociocultural experiences as a personalization factor for the image content used within graphical password schemes, since it has a positive impact on the security and memorability of the user-chosen passwords. This paper discusses the need for personalization of the image content used in graphical password schemes, as well as the initial steps towards the realization of an image content personalization framework that aims to achieve a better equilibrium between security and memorability. The paper also discusses emerging challenges related to the elicitation and maintenance of individual sociocultural-centered user models, the image content personalization mechanism and privacy considerations.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132699110","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}
Maria Schmidt, David Helbig, Ojashree Bhandare, D. Stier, W. Minker, S. Werner
Using Personal Assistants (PAs) via voice becomes increasingly usual as more and more devices in different environments offer this capability, such as Google Assistant, Amazon Alexa, Apple Siri, Microsoft Cortana, Mercedes-Benz MBUX or BMW Intelligent Personal Assistant. PAs help users to set reminders, find their way through traffic, or send messages to friends and colleagues. While serving the users' needs, PAs constantly collect personal data in order to personalize their services and adapt their behavior. In order to find out which objective Cognitive Load (CL) indicators reflect the users' perception of proactive system behavior in six specific use cases of an in-car PA, we conducted a Wizard of Oz study in a driving simulator with 42 participants. We varied traffic density and tracked physiological data, such as heart rate (HR) and electrodermal activity (EDA). We assessed the users' CL during the interaction with the PA by employing these data as well as real-time driving data (RTDA) via the Controller Area Network (CAN bus). The results show that physiological data like HR and EDA are not suitable to be used as indicators for the users' CL in this experiment. This is because the tracked physiological data do not show significant differences with respect to different traffic densities or proactivity. At the same time it has to be discussed whether the used type of recording physiological data is robust enough for our purposes. Concerning driving data, only the acceleration parameter showed a tendency towards differences between age groups, though insignificantly. The same is valid for the steering angle parameter when comparing male and female users. For future work, we plan to additionally evaluate subjective CL measures and other ratings to see whether these show more significant differences between the (non-)proactive assistants, traffic densities, or use cases.
{"title":"Assessing Objective Indicators of Users' Cognitive Load During Proactive In-Car Dialogs","authors":"Maria Schmidt, David Helbig, Ojashree Bhandare, D. Stier, W. Minker, S. Werner","doi":"10.1145/3314183.3324985","DOIUrl":"https://doi.org/10.1145/3314183.3324985","url":null,"abstract":"Using Personal Assistants (PAs) via voice becomes increasingly usual as more and more devices in different environments offer this capability, such as Google Assistant, Amazon Alexa, Apple Siri, Microsoft Cortana, Mercedes-Benz MBUX or BMW Intelligent Personal Assistant. PAs help users to set reminders, find their way through traffic, or send messages to friends and colleagues. While serving the users' needs, PAs constantly collect personal data in order to personalize their services and adapt their behavior. In order to find out which objective Cognitive Load (CL) indicators reflect the users' perception of proactive system behavior in six specific use cases of an in-car PA, we conducted a Wizard of Oz study in a driving simulator with 42 participants. We varied traffic density and tracked physiological data, such as heart rate (HR) and electrodermal activity (EDA). We assessed the users' CL during the interaction with the PA by employing these data as well as real-time driving data (RTDA) via the Controller Area Network (CAN bus). The results show that physiological data like HR and EDA are not suitable to be used as indicators for the users' CL in this experiment. This is because the tracked physiological data do not show significant differences with respect to different traffic densities or proactivity. At the same time it has to be discussed whether the used type of recording physiological data is robust enough for our purposes. Concerning driving data, only the acceleration parameter showed a tendency towards differences between age groups, though insignificantly. The same is valid for the steering angle parameter when comparing male and female users. For future work, we plan to additionally evaluate subjective CL measures and other ratings to see whether these show more significant differences between the (non-)proactive assistants, traffic densities, or use cases.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"9 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671459","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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