Pub Date : 2019-06-20DOI: 10.3389/fcomp.2019.00001
M. C. J. Lizandra, Jorge L. Charco, Inmaculada García-García, R. Mollá
Healthy eating habits involve controlling your diet. It is important to know how to interpret the nutritional information of the packaged foods that you consume. These packaged foods are usually processed and contain carbohydrates and fats. Monitoring carbohydrates intake is particularly important for weight-loss diets and for some pathologies such as diabetes. In this paper, we present an augmented reality app for helping interpret the nutritional information about carbohydrates in real packaged foods with the shape of boxes or cans. The app tracks the full object and guides the user in finding the surface or area of the real package where the information about carbohydrates is located using augmented reality and helps the user to interpret this information. The portions of carbohydrates (also called carb choices or carb servings) that correspond to the visualized food are shown. We carried out a study to check the effectiveness of our app regarding learning outcomes, usability, and perceived satisfaction. A total of 40 people participated in the study (20 men and 20 women). The participants were between 14 and 55 years old. The results reported that their initial knowledge about carb choices was very low. This indicates that education about nutritional information in packaged foods is needed. An analysis of the pre-knowledge and post-knowledge questionnaires showed that the users had a statistically significant increase in knowledge about carb choices using our app. Gender and age did not influence the knowledge acquired. The participants were highly satisfied with our app. In conclusion, our app and similar apps could be used to effectively learn how to interpret the nutritional information on the labels of real packaged foods and thus help users acquire healthy life habits.
{"title":"An Augmented Reality App to Learn to Interpret the Nutritional Information on Labels of Real Packaged Foods","authors":"M. C. J. Lizandra, Jorge L. Charco, Inmaculada García-García, R. Mollá","doi":"10.3389/fcomp.2019.00001","DOIUrl":"https://doi.org/10.3389/fcomp.2019.00001","url":null,"abstract":"Healthy eating habits involve controlling your diet. It is important to know how to interpret the nutritional information of the packaged foods that you consume. These packaged foods are usually processed and contain carbohydrates and fats. Monitoring carbohydrates intake is particularly important for weight-loss diets and for some pathologies such as diabetes. In this paper, we present an augmented reality app for helping interpret the nutritional information about carbohydrates in real packaged foods with the shape of boxes or cans. The app tracks the full object and guides the user in finding the surface or area of the real package where the information about carbohydrates is located using augmented reality and helps the user to interpret this information. The portions of carbohydrates (also called carb choices or carb servings) that correspond to the visualized food are shown. We carried out a study to check the effectiveness of our app regarding learning outcomes, usability, and perceived satisfaction. A total of 40 people participated in the study (20 men and 20 women). The participants were between 14 and 55 years old. The results reported that their initial knowledge about carb choices was very low. This indicates that education about nutritional information in packaged foods is needed. An analysis of the pre-knowledge and post-knowledge questionnaires showed that the users had a statistically significant increase in knowledge about carb choices using our app. Gender and age did not influence the knowledge acquired. The participants were highly satisfied with our app. In conclusion, our app and similar apps could be used to effectively learn how to interpret the nutritional information on the labels of real packaged foods and thus help users acquire healthy life habits.","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133508411","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 : 2019-05-25DOI: 10.3389/fcomp.2019.00006
Constantin Pape, A. Matskevych, A. Wolny, Julian Hennies, Giulia Mizzon, Marion Louveaux, J. Musser, A. Maizel, D. Arendt, A. Kreshuk
The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural networks, their accuracy still lags behind human annotations and requires additional manual proof-reading. A major hindrance to further improvements is the limited field of view of the segmentation networks preventing them from exploiting the expected cell morphology or other prior biological knowledge which humans use to inform their segmentation decisions. In this contribution, we show how such domain-specific information can be leveraged by expressing it as long-range interactions in a graph partitioning problem known as the lifted multicut problem. Using this formulation, we demonstrate significant improvement in segmentation accuracy for three challenging EM segmentation problems from neuroscience and cell biology.
{"title":"Leveraging Domain Knowledge to Improve Microscopy Image Segmentation With Lifted Multicuts","authors":"Constantin Pape, A. Matskevych, A. Wolny, Julian Hennies, Giulia Mizzon, Marion Louveaux, J. Musser, A. Maizel, D. Arendt, A. Kreshuk","doi":"10.3389/fcomp.2019.00006","DOIUrl":"https://doi.org/10.3389/fcomp.2019.00006","url":null,"abstract":"The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural networks, their accuracy still lags behind human annotations and requires additional manual proof-reading. A major hindrance to further improvements is the limited field of view of the segmentation networks preventing them from exploiting the expected cell morphology or other prior biological knowledge which humans use to inform their segmentation decisions. In this contribution, we show how such domain-specific information can be leveraged by expressing it as long-range interactions in a graph partitioning problem known as the lifted multicut problem. Using this formulation, we demonstrate significant improvement in segmentation accuracy for three challenging EM segmentation problems from neuroscience and cell biology.","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443236","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 : 2019-03-27DOI: 10.3389/fcomp.2019.00002
Naoki Nishimura, K. Tanahashi, Koji Suganuma, Masamichi J. Miyama, Masayuki Ohzeki
For e-commerce websites, deciding the manner in which items are listed on webpages is an important issue because it can dramatically affect item sales. One of the simplest strategies of listing items to improve the overall sales is to do so in a descending order of sales or sales numbers. However, in lists generated using this strategy, items with high similarity are often placed consecutively. In other words, the generated item list might be biased toward a specific preference. Therefore, this study employs penalties for items with high similarity being placed next to each other in the list and transforms the item listing problem to a quadratic assignment problem (QAP). The QAP is well-known as an NP-hard problem that cannot be solved in polynomial time. To solve the QAP, we employ quantum annealing (QA), which exploits the quantum tunneling effect to efficiently solve an optimization problem. In addition, we propose a problem decomposition method based on the structure of the item listing problem because the quantum annealer we use (i.e., D-Wave 2000Q) has a limited number of quantum bits. Our experimental results indicate that we can create an item list that considers both sales and diversity. In addition, we observe that using the problem decomposition method based on a problem structure can lead to a better solution with the quantum annealer in comparison with the existing problem decomposition method.
{"title":"Item Listing Optimization for E-Commerce Websites Based on Diversity","authors":"Naoki Nishimura, K. Tanahashi, Koji Suganuma, Masamichi J. Miyama, Masayuki Ohzeki","doi":"10.3389/fcomp.2019.00002","DOIUrl":"https://doi.org/10.3389/fcomp.2019.00002","url":null,"abstract":"For e-commerce websites, deciding the manner in which items are listed on webpages is an important issue because it can dramatically affect item sales. One of the simplest strategies of listing items to improve the overall sales is to do so in a descending order of sales or sales numbers. However, in lists generated using this strategy, items with high similarity are often placed consecutively. In other words, the generated item list might be biased toward a specific preference. Therefore, this study employs penalties for items with high similarity being placed next to each other in the list and transforms the item listing problem to a quadratic assignment problem (QAP). The QAP is well-known as an NP-hard problem that cannot be solved in polynomial time. To solve the QAP, we employ quantum annealing (QA), which exploits the quantum tunneling effect to efficiently solve an optimization problem. In addition, we propose a problem decomposition method based on the structure of the item listing problem because the quantum annealer we use (i.e., D-Wave 2000Q) has a limited number of quantum bits. Our experimental results indicate that we can create an item list that considers both sales and diversity. In addition, we observe that using the problem decomposition method based on a problem structure can lead to a better solution with the quantum annealer in comparison with the existing problem decomposition method.","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125133356","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 : 1900-01-01DOI: 10.3389/fcomp.2021.793466
L. D. Wet
In teaching Human-Computer Interaction at university level, it has always been beneficial to explain the related theory and engage students in a practical way, whether individually or in groups. And then came COVID-19. Face-to-face classes were replaced by emergency remote teaching methods. Students became student numbers in cyber space. The danger became real to convert back to the traditional way of presenting lectures, namely a lecturer doing all the talking and the students being the passive audience. This paper describes how the author had to adapt and innovate in terms of teaching Human-Computer Interaction modules to university students in a practical way during the COVID-19 pandemic. Frequent online quizzes, audio messages, online group discussion, smaller topic-dedicated practical activities, and webinars encouraging student participation, were employed. Instead of having access to eye-tracking technology in a usability laboratory, students had to innovate for usability evaluation assignments by employing observation, think-aloud protocols, and performance and self-reported metrics as data gathering methods. The laboratory had to be replaced by COVID-compliant places of residence. The outcomes of adapting previously-used teaching methods and inventing new ways to encourage student participation, were surprisingly positive. An additional advantage was that many of these methods turned out to be so successful that their application could be continued and extended to post-pandemic times for a blended learning approach to further enrich Human-Computer Interaction teaching.
{"title":"Teaching Human-Computer Interaction Modules - And Then Came COVID-19","authors":"L. D. Wet","doi":"10.3389/fcomp.2021.793466","DOIUrl":"https://doi.org/10.3389/fcomp.2021.793466","url":null,"abstract":"In teaching Human-Computer Interaction at university level, it has always been beneficial to explain the related theory and engage students in a practical way, whether individually or in groups. And then came COVID-19. Face-to-face classes were replaced by emergency remote teaching methods. Students became student numbers in cyber space. The danger became real to convert back to the traditional way of presenting lectures, namely a lecturer doing all the talking and the students being the passive audience. This paper describes how the author had to adapt and innovate in terms of teaching Human-Computer Interaction modules to university students in a practical way during the COVID-19 pandemic. Frequent online quizzes, audio messages, online group discussion, smaller topic-dedicated practical activities, and webinars encouraging student participation, were employed. Instead of having access to eye-tracking technology in a usability laboratory, students had to innovate for usability evaluation assignments by employing observation, think-aloud protocols, and performance and self-reported metrics as data gathering methods. The laboratory had to be replaced by COVID-compliant places of residence. The outcomes of adapting previously-used teaching methods and inventing new ways to encourage student participation, were surprisingly positive. An additional advantage was that many of these methods turned out to be so successful that their application could be continued and extended to post-pandemic times for a blended learning approach to further enrich Human-Computer Interaction teaching.","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124429073","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 : 1900-01-01DOI: 10.3389/fcomp.2022.890531
A. Gomez-Marin
{"title":"Commentary: Metaphors We Live By","authors":"A. Gomez-Marin","doi":"10.3389/fcomp.2022.890531","DOIUrl":"https://doi.org/10.3389/fcomp.2022.890531","url":null,"abstract":"","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126540263","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 : 1900-01-01DOI: 10.3389/fcomp.2022.899507
J. V. Erp
Implicit physiological measures such as heart rate and skin conductance convey information about someone's cognitive or affective state. Currently, gastrointestinal (GI) tract-based markers are not yet considered while both the organs involved as well as the microbiota populating the GI tract are bidirectionally connected to the brain and have a relation to emotion, cognition and behavior. This makes GI tract-based measures relevant and interesting, especially because the relation may be causal, and because they have a different timescale than current physiological measures. This perspective paper (1) presents the (mechanistic) involvement of the GI tract and its microbiota in emotion, cognition and behavior; (2) explores the added value of microbiome-based implicit measures as complementary to existing measures; and (3) sets the priorities to move forward. Five potential measures are proposed and discussed in more detail: bowel movement, short-chain fatty acids, tyrosine and tryptophan, GI tract flora composition, and cytokine levels. We conclude (1) that the involvement of the GI tract in emotion, cognition and behavior is undisputed, (2) that GI tract-based implicit measures are still in a conceptual phase of development but show potential and (3) that the first step to bring this field forward is to start validation studies in healthy humans and that are designed in the context of implicit measurements.
{"title":"Gastrointestinal tract-based implicit measures for cognition, emotion and behavior","authors":"J. V. Erp","doi":"10.3389/fcomp.2022.899507","DOIUrl":"https://doi.org/10.3389/fcomp.2022.899507","url":null,"abstract":"Implicit physiological measures such as heart rate and skin conductance convey information about someone's cognitive or affective state. Currently, gastrointestinal (GI) tract-based markers are not yet considered while both the organs involved as well as the microbiota populating the GI tract are bidirectionally connected to the brain and have a relation to emotion, cognition and behavior. This makes GI tract-based measures relevant and interesting, especially because the relation may be causal, and because they have a different timescale than current physiological measures. This perspective paper (1) presents the (mechanistic) involvement of the GI tract and its microbiota in emotion, cognition and behavior; (2) explores the added value of microbiome-based implicit measures as complementary to existing measures; and (3) sets the priorities to move forward. Five potential measures are proposed and discussed in more detail: bowel movement, short-chain fatty acids, tyrosine and tryptophan, GI tract flora composition, and cytokine levels. We conclude (1) that the involvement of the GI tract in emotion, cognition and behavior is undisputed, (2) that GI tract-based implicit measures are still in a conceptual phase of development but show potential and (3) that the first step to bring this field forward is to start validation studies in healthy humans and that are designed in the context of implicit measurements.","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125555058","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 : 1900-01-01DOI: 10.3389/fcomp.2021.691622
Kristof Van Laerhoven
A long lasting challenge in wearable and ubiquitous computing has been to bridge the interaction gap between the users and their manifold computers. How can we as humans easily perceive and interpret contextual information? Noticing whether someone is bored, stressed, busy, or fascinated in face-to-face interactions, is still largely unsolved for computers in everyday life. The first message of this article is that much of the research of the past decades aiming to alleviate this context gap between computers and their users, has clustered into three fields. The aim is to model human users in different observable categories (alphabetically ordered): Activity, Affect, and Attention. A second important point to make is that the research fields aiming for machine recognition of these three A’s, thus far have had only a limited amount of overlap, but are bound to converge in terms of methodology and from a systems perspective. A final point then concludes with the following call to action: A consequence of such a possible merger between the three A’s is the need for a more consolidated way of performing solid, reproducible research studies. These fields can learn from each other’s best practices, and their interaction can both lead to the creation of overarching benchmarks, as well as establish common data pipelines. The opportunities are plenty. As early as 1960, J. C. R. Licklider regarded the symbiosis between human and machine as a flourishing field of research to come: “A multidisciplinary study group, examining future research and development problems of the Air Force, estimated that it would be 1980 before developments in artificial intelligence make it possible for machines alone to do much thinking or problem solving of military significance. That would leave, say, 5 years to develop mancomputer symbiosis and 15 years to use it. The 15 may be 10 or 500, but those years should be intellectually the most creative and exciting in the history of mankind.” (Licklider, 1960). Advances in Machine Learning, Deep Learning and Sensors Research have shown in the past years that computers have mastered many problem domains. Computers have improved immensely in tasks such as spotting objects from camera footage, or inferring our vital signs from miniature sensors placed on our skins. Keeping track of what the system’s user is doing (Activity), how they are feeling (Affect), and what they are focusing on (Attention), has proven a much more difficult task. There is no sensor that directly can measure even one of these A’s, and there are thus far no models for them to facilitate their machine recognition. This makes the three A’s an ideal “holy grail” to aim for, likely for the upcoming decade. The automatic detection of a user’s Activity, Affect, and Attention is on one hand more specific than the similar research field of context awareness (Schmidt et al., 1999), yet challenging and well-defined enough to spur (and require) multi-disciplinary and high-qua
{"title":"The Three A's of Wearable and Ubiquitous Computing: Activity, Affect, and Attention","authors":"Kristof Van Laerhoven","doi":"10.3389/fcomp.2021.691622","DOIUrl":"https://doi.org/10.3389/fcomp.2021.691622","url":null,"abstract":"A long lasting challenge in wearable and ubiquitous computing has been to bridge the interaction gap between the users and their manifold computers. How can we as humans easily perceive and interpret contextual information? Noticing whether someone is bored, stressed, busy, or fascinated in face-to-face interactions, is still largely unsolved for computers in everyday life. The first message of this article is that much of the research of the past decades aiming to alleviate this context gap between computers and their users, has clustered into three fields. The aim is to model human users in different observable categories (alphabetically ordered): Activity, Affect, and Attention. A second important point to make is that the research fields aiming for machine recognition of these three A’s, thus far have had only a limited amount of overlap, but are bound to converge in terms of methodology and from a systems perspective. A final point then concludes with the following call to action: A consequence of such a possible merger between the three A’s is the need for a more consolidated way of performing solid, reproducible research studies. These fields can learn from each other’s best practices, and their interaction can both lead to the creation of overarching benchmarks, as well as establish common data pipelines. The opportunities are plenty. As early as 1960, J. C. R. Licklider regarded the symbiosis between human and machine as a flourishing field of research to come: “A multidisciplinary study group, examining future research and development problems of the Air Force, estimated that it would be 1980 before developments in artificial intelligence make it possible for machines alone to do much thinking or problem solving of military significance. That would leave, say, 5 years to develop mancomputer symbiosis and 15 years to use it. The 15 may be 10 or 500, but those years should be intellectually the most creative and exciting in the history of mankind.” (Licklider, 1960). Advances in Machine Learning, Deep Learning and Sensors Research have shown in the past years that computers have mastered many problem domains. Computers have improved immensely in tasks such as spotting objects from camera footage, or inferring our vital signs from miniature sensors placed on our skins. Keeping track of what the system’s user is doing (Activity), how they are feeling (Affect), and what they are focusing on (Attention), has proven a much more difficult task. There is no sensor that directly can measure even one of these A’s, and there are thus far no models for them to facilitate their machine recognition. This makes the three A’s an ideal “holy grail” to aim for, likely for the upcoming decade. The automatic detection of a user’s Activity, Affect, and Attention is on one hand more specific than the similar research field of context awareness (Schmidt et al., 1999), yet challenging and well-defined enough to spur (and require) multi-disciplinary and high-qua","PeriodicalId":305963,"journal":{"name":"Frontiers Comput. Sci.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122055530","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：