Pub Date : 2024-02-29DOI: 10.3389/fcomp.2024.1368362
Ayşe Nurdan Saran
A password hashing algorithm is a cryptographic method that transforms passwords into a secure and irreversible format. It is used not only for authentication purposes but also for key derivation mechanisms. The primary purpose of password hashing is to enhance the security of user credentials by preventing the exposure of plaintext passwords in the event of a data breach. As a key derivation function, password hashing aims to derive secret keys from a master key, password, or passphrase using a pseudorandom function. This review focuses on the design and analysis of time-memory trade-off (TMTO) attacks on recent password hashing algorithms. This review presents a comprehensive survey of TMTO attacks and recent studies on password hashing for authentication by examining the literature. The study provides valuable insights and strategies for safely navigating transitions, emphasizing the importance of a systematic approach and thorough testing to mitigate risk. The purpose of this paper is to provide guidance to developers and administrators on how to update cryptographic practices in response to evolving security standards and threats.
{"title":"On time-memory trade-offs for password hashing schemes","authors":"Ayşe Nurdan Saran","doi":"10.3389/fcomp.2024.1368362","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1368362","url":null,"abstract":"A password hashing algorithm is a cryptographic method that transforms passwords into a secure and irreversible format. It is used not only for authentication purposes but also for key derivation mechanisms. The primary purpose of password hashing is to enhance the security of user credentials by preventing the exposure of plaintext passwords in the event of a data breach. As a key derivation function, password hashing aims to derive secret keys from a master key, password, or passphrase using a pseudorandom function. This review focuses on the design and analysis of time-memory trade-off (TMTO) attacks on recent password hashing algorithms. This review presents a comprehensive survey of TMTO attacks and recent studies on password hashing for authentication by examining the literature. The study provides valuable insights and strategies for safely navigating transitions, emphasizing the importance of a systematic approach and thorough testing to mitigate risk. The purpose of this paper is to provide guidance to developers and administrators on how to update cryptographic practices in response to evolving security standards and threats.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"143 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140415533","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 : 2024-02-29DOI: 10.3389/fcomp.2024.1304288
Atul Rana, Sachin Gupta, Bhoomi Gupta
Attack trees are a widely used method for threat modeling and analyzing cyber-attacks in organizational networks. Assessing the risk associated with each individual node of an attack tree is crucial for understanding the overall risk of the attack. This article presents a comparative study of different threat modeling methods and risk assessment approaches in organizational networks. The article also presents a novel comprehensive approach for quantifying risk assessment of organizational networks based on attack trees modified according to the factor analysis of information risk (FAIR) approach. Our results demonstrate the effectiveness of the novel approach in capturing the unique characteristics of different assets and their dependencies in an attack tree, leading to quantitative risk assessment.
{"title":"A comprehensive framework for quantitative risk assessment of organizational networks using FAIR-modified attack trees","authors":"Atul Rana, Sachin Gupta, Bhoomi Gupta","doi":"10.3389/fcomp.2024.1304288","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1304288","url":null,"abstract":"Attack trees are a widely used method for threat modeling and analyzing cyber-attacks in organizational networks. Assessing the risk associated with each individual node of an attack tree is crucial for understanding the overall risk of the attack. This article presents a comparative study of different threat modeling methods and risk assessment approaches in organizational networks. The article also presents a novel comprehensive approach for quantifying risk assessment of organizational networks based on attack trees modified according to the factor analysis of information risk (FAIR) approach. Our results demonstrate the effectiveness of the novel approach in capturing the unique characteristics of different assets and their dependencies in an attack tree, leading to quantitative risk assessment.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"2012 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140416257","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 : 2024-02-29DOI: 10.3389/fcomp.2024.1347424
Lena Uhlenberg, Oliver Amft
We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average < 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.
{"title":"Where to mount the IMU? Validation of joint angle kinematics and sensor selection for activities of daily living","authors":"Lena Uhlenberg, Oliver Amft","doi":"10.3389/fcomp.2024.1347424","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1347424","url":null,"abstract":"We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average < 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"14 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140410046","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 : 2024-02-29DOI: 10.3389/fcomp.2024.1326452
Javier Jareño, G. Bárcena-González, J. Castro-Gutiérrez, R. Cabrera-Castro, Pedro L. Galindo
Convolutional neural networks (CNNs) have revolutionized image recognition. Their ability to identify complex patterns, combined with learning transfer techniques, has proven effective in multiple fields, such as image classification. In this article we propose to apply a two-step methodology for image classification tasks. First, apply transfer learning with the desired dataset, and subsequently, in a second stage, replace the classification layers by other alternative classification models. The whole methodology has been tested on a dataset collected at Conil de la Frontera fish market, in Southwest Spain, including 19 different fish species to be classified for fish auction market. The study was conducted in five steps: (i) collecting and preprocessing images included in the dataset, (ii) using transfer learning from 4 well-known CNNs (ResNet152V2, VGG16, EfficientNetV2L and Xception) for image classification to get initial models, (iii) apply fine-tuning to obtain final CNN models, (iv) substitute classification layer with 21 different classifiers obtaining multiple F1-scores for different training-test splits of the dataset for each model, and (v) apply post-hoc statistical analysis to compare their performances in terms of accuracy. Results indicate that combining the feature extraction capabilities of CNNs with other supervised classification algorithms, such as Support Vector Machines or Linear Discriminant Analysis is a simple and effective way to increase model performance.
{"title":"Automatic labeling of fish species using deep learning across different classification strategies","authors":"Javier Jareño, G. Bárcena-González, J. Castro-Gutiérrez, R. Cabrera-Castro, Pedro L. Galindo","doi":"10.3389/fcomp.2024.1326452","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1326452","url":null,"abstract":"Convolutional neural networks (CNNs) have revolutionized image recognition. Their ability to identify complex patterns, combined with learning transfer techniques, has proven effective in multiple fields, such as image classification. In this article we propose to apply a two-step methodology for image classification tasks. First, apply transfer learning with the desired dataset, and subsequently, in a second stage, replace the classification layers by other alternative classification models. The whole methodology has been tested on a dataset collected at Conil de la Frontera fish market, in Southwest Spain, including 19 different fish species to be classified for fish auction market. The study was conducted in five steps: (i) collecting and preprocessing images included in the dataset, (ii) using transfer learning from 4 well-known CNNs (ResNet152V2, VGG16, EfficientNetV2L and Xception) for image classification to get initial models, (iii) apply fine-tuning to obtain final CNN models, (iv) substitute classification layer with 21 different classifiers obtaining multiple F1-scores for different training-test splits of the dataset for each model, and (v) apply post-hoc statistical analysis to compare their performances in terms of accuracy. Results indicate that combining the feature extraction capabilities of CNNs with other supervised classification algorithms, such as Support Vector Machines or Linear Discriminant Analysis is a simple and effective way to increase model performance.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140416083","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 : 2024-02-27DOI: 10.3389/fcomp.2024.1369550
Teresa Romão, Sergi Bermúdez i Badia
{"title":"Editorial: Entertainment computing and persuasive technologies","authors":"Teresa Romão, Sergi Bermúdez i Badia","doi":"10.3389/fcomp.2024.1369550","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1369550","url":null,"abstract":"","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"56 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427734","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 : 2024-02-23DOI: 10.3389/fcomp.2024.1283735
Rafal Kocielnik, Zhuofang Li, Claudia Kann, D. Sambrano, Jacob Morrier, Mitchell Linegar, Carly Taylor, Min Kim, Nabiha Naqvie, Feri Soltani, Arman Dehpanah, Grant Cahill, Animashree Anandkumar, R. M. Alvarez
Online competitive action games are a very popular form of entertainment. While most are respectfully enjoyed by millions of players, a small group of players engages in disruptive behavior, such as cheating and hate speech. Identifying and subsequently moderating these toxic players is a challenging task. Previous research has only studied specific aspects of this problem using curated data and with limited access to real-world moderation practices. In contrast, our work offers a unique and holistic view of the universal challenges of moderating disruptive behavior in online systems. We combine an analysis of a large dataset from a popular online competitive first-person action title (Call of Duty®: Modern Warfare®II) with insights from stakeholders involved in moderation. We identify six universal challenges related to handling disruptive behaviors in such games. We discuss challenges omitted by prior work, such as handling high-volume imbalanced data or ensuring the comfort of human moderators. We also offer a discussion of possible technical, design, and policy approaches to mitigating these challenges.
{"title":"Challenges in moderating disruptive player behavior in online competitive action games","authors":"Rafal Kocielnik, Zhuofang Li, Claudia Kann, D. Sambrano, Jacob Morrier, Mitchell Linegar, Carly Taylor, Min Kim, Nabiha Naqvie, Feri Soltani, Arman Dehpanah, Grant Cahill, Animashree Anandkumar, R. M. Alvarez","doi":"10.3389/fcomp.2024.1283735","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1283735","url":null,"abstract":"Online competitive action games are a very popular form of entertainment. While most are respectfully enjoyed by millions of players, a small group of players engages in disruptive behavior, such as cheating and hate speech. Identifying and subsequently moderating these toxic players is a challenging task. Previous research has only studied specific aspects of this problem using curated data and with limited access to real-world moderation practices. In contrast, our work offers a unique and holistic view of the universal challenges of moderating disruptive behavior in online systems. We combine an analysis of a large dataset from a popular online competitive first-person action title (Call of Duty®: Modern Warfare®II) with insights from stakeholders involved in moderation. We identify six universal challenges related to handling disruptive behaviors in such games. We discuss challenges omitted by prior work, such as handling high-volume imbalanced data or ensuring the comfort of human moderators. We also offer a discussion of possible technical, design, and policy approaches to mitigating these challenges.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"26 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957336","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 : 2024-02-22DOI: 10.3389/fcomp.2024.1343139
Jeffrey N. Gerwin, Gustavo de Oliveira Almeida, Michael W. Boyce, Melissa Joseph, Ambrose H. Wong, Winslow Burleson, Leigh V. Evans
The purpose of this study was to address the logistical and data challenges of using wearable technologies in the context of a clinical trial to measure heart rate variability (HRV) as a marker of physiologic stress in emergency healthcare providers during the COVID-19 pandemic. When using these wearable smart garments, the dilemma is two-fold: (1) the volume of raw physiological data produced is enormous and is recorded in formats not easily portable in standard analytic software, and (2) the commensurate data analysis often requires proprietary software. Our team iteratively developed a novel algorithm called HRVEST that can successfully process enormous volumes of physiologic raw data generated by wearable smart garments and meet the specific needs of HRV analyses. HRVEST is a noise-filtering and data-processing algorithm that allows the precise measurements of heart rate variability (HRV) of clinicians working in an Emergency Department (ED). HRVEST automatically processed the biometric data derived from 413 electrocardiogram (ECG) recordings in just over 15 min. Furthermore, throughout this study, we identified unique challenges of working with these technologies and proposed solutions that may facilitate future use in broader contexts. With HRVEST, using wearable smart garments to monitor HRV over long periods of time becomes logistically and feasibly viable for future studies. We also see the potential for real-time feedback to prophylactically reduce emergency physician stress, like informing optimal break-taking or short meditation sessions to lower heart rate. This could improve emotional wellbeing and, subsequently, clinical decision-making and patient outcomes.
{"title":"HRVEST: a novel data solution for using wearable smart technology to measure physiologic stress variables during a randomized clinical trial","authors":"Jeffrey N. Gerwin, Gustavo de Oliveira Almeida, Michael W. Boyce, Melissa Joseph, Ambrose H. Wong, Winslow Burleson, Leigh V. Evans","doi":"10.3389/fcomp.2024.1343139","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1343139","url":null,"abstract":"The purpose of this study was to address the logistical and data challenges of using wearable technologies in the context of a clinical trial to measure heart rate variability (HRV) as a marker of physiologic stress in emergency healthcare providers during the COVID-19 pandemic. When using these wearable smart garments, the dilemma is two-fold: (1) the volume of raw physiological data produced is enormous and is recorded in formats not easily portable in standard analytic software, and (2) the commensurate data analysis often requires proprietary software. Our team iteratively developed a novel algorithm called HRVEST that can successfully process enormous volumes of physiologic raw data generated by wearable smart garments and meet the specific needs of HRV analyses. HRVEST is a noise-filtering and data-processing algorithm that allows the precise measurements of heart rate variability (HRV) of clinicians working in an Emergency Department (ED). HRVEST automatically processed the biometric data derived from 413 electrocardiogram (ECG) recordings in just over 15 min. Furthermore, throughout this study, we identified unique challenges of working with these technologies and proposed solutions that may facilitate future use in broader contexts. With HRVEST, using wearable smart garments to monitor HRV over long periods of time becomes logistically and feasibly viable for future studies. We also see the potential for real-time feedback to prophylactically reduce emergency physician stress, like informing optimal break-taking or short meditation sessions to lower heart rate. This could improve emotional wellbeing and, subsequently, clinical decision-making and patient outcomes.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"72 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140439152","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 : 2024-02-21DOI: 10.3389/fcomp.2024.1258289
A. N. Nagele, Julian Hough
Sleep-tracking products are promising their users an improvement to their sleep by focusing on behavior change but often neglecting the contextual and individual factors contributing to sleep quality and quantity. Making good sleep for productive scheduling a personal responsibility does not necessarily lead to better sleep and may cause stress and anxiety. In an autoethnographic study, the first author of this paper tracked her sleep for one month using a diary, body maps and an Oura ring and compared her subjectively felt sleep experience with the data produced by the Oura app. A thematic analysis of the data resulted in four themes describing the relationship between the user-researcher and her wearable sleep-tracker: (1) good sleep scores are motivating, (2) experience that matches the data leads to sense-making, (3) contradictory information from the app leads to frustration, and (4) the sleep-tracker competes with other social agents. A diffractive reading of the data and research process, following Karen Barad's methodology, resulted in a discussion of how data passes through the analog and digital apparatus and what contextual factors are left out but still significantly impact sleep quality and quantity. We add to a canon of sleep research recommending a move away from representing sleep in terms of comparison and competition, uncoupling it from neoliberal capitalistic productivity and self-improvement narratives which are often key contributing factors to bad sleep in the first place.
{"title":"“The sleep data looks way better than I feel.” An autoethnographic account and diffractive reading of sleep-tracking","authors":"A. N. Nagele, Julian Hough","doi":"10.3389/fcomp.2024.1258289","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1258289","url":null,"abstract":"Sleep-tracking products are promising their users an improvement to their sleep by focusing on behavior change but often neglecting the contextual and individual factors contributing to sleep quality and quantity. Making good sleep for productive scheduling a personal responsibility does not necessarily lead to better sleep and may cause stress and anxiety. In an autoethnographic study, the first author of this paper tracked her sleep for one month using a diary, body maps and an Oura ring and compared her subjectively felt sleep experience with the data produced by the Oura app. A thematic analysis of the data resulted in four themes describing the relationship between the user-researcher and her wearable sleep-tracker: (1) good sleep scores are motivating, (2) experience that matches the data leads to sense-making, (3) contradictory information from the app leads to frustration, and (4) the sleep-tracker competes with other social agents. A diffractive reading of the data and research process, following Karen Barad's methodology, resulted in a discussion of how data passes through the analog and digital apparatus and what contextual factors are left out but still significantly impact sleep quality and quantity. We add to a canon of sleep research recommending a move away from representing sleep in terms of comparison and competition, uncoupling it from neoliberal capitalistic productivity and self-improvement narratives which are often key contributing factors to bad sleep in the first place.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443443","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 : 2024-02-21DOI: 10.3389/fcomp.2024.1187933
Nicolo Merendino, Antonio Rodà, Raul Masu
The project presented in this paper illustrates the design process for the development of an IoT system that monitors a specific bio-metric parameter (heart rate) in real time and provides feedback for an opera singer, as well as adding effects that manipulate the sounds emitted by the body during a self-healing practice. This allows the singer to rest and alternate opera singing techniques (which is very demanding) with other less demanding singing techniques and even a self-healing session in case of necessity during a performance. The case study presented in this paper has been developed with and for Eleonora Amianto, an opera singer who suffered from a carotid aneurysm. We performed an idiographic design process, closely collaborating with Eleonora, and developed a wearable IoT that suited her health and artistic needs. In the design of the system, we explore the intersection between self-healthcare and performative arts, focusing on the use of an Internet of Musical Things (IoMusT) system to implement medical prevention and treatment practices in an art performance. The system is developed using open-source tools, allowing for easy replication and improvement, as well as reducing risks of obsolescence and costs of updating. We complement a formal evaluation session with field notes collected during the design phase. We could observe a positive effect of the system on Eleonora's practice and its potential applications within different performative scenarios.
{"title":"“Below 58 BPM,” involving real-time monitoring and self-medication practices in music performance through IoT technology","authors":"Nicolo Merendino, Antonio Rodà, Raul Masu","doi":"10.3389/fcomp.2024.1187933","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1187933","url":null,"abstract":"The project presented in this paper illustrates the design process for the development of an IoT system that monitors a specific bio-metric parameter (heart rate) in real time and provides feedback for an opera singer, as well as adding effects that manipulate the sounds emitted by the body during a self-healing practice. This allows the singer to rest and alternate opera singing techniques (which is very demanding) with other less demanding singing techniques and even a self-healing session in case of necessity during a performance. The case study presented in this paper has been developed with and for Eleonora Amianto, an opera singer who suffered from a carotid aneurysm. We performed an idiographic design process, closely collaborating with Eleonora, and developed a wearable IoT that suited her health and artistic needs. In the design of the system, we explore the intersection between self-healthcare and performative arts, focusing on the use of an Internet of Musical Things (IoMusT) system to implement medical prevention and treatment practices in an art performance. The system is developed using open-source tools, allowing for easy replication and improvement, as well as reducing risks of obsolescence and costs of updating. We complement a formal evaluation session with field notes collected during the design phase. We could observe a positive effect of the system on Eleonora's practice and its potential applications within different performative scenarios.","PeriodicalId":510141,"journal":{"name":"Frontiers in Computer Science","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140444062","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 : 2024-02-20DOI: 10.3389/fcomp.2024.1274828
S. G. Wheeler, S. Hoermann, S. Lukosch, Robert W. Lindeman
The use of virtual reality (VR) in firefighter training is promising because it provides cost-effective, safe environments that arouse similar behavioral responses to real-life scenarios. However, the pedagogical potential of VR and its impact on learning outcomes compared to traditional methods is currently an under-explored area. This research investigates how well VR can support learning compared to traditional methods in the context of training firefighters in combating vegetation fires. A VR learning environment was developed, informed by a “design for learning” framework providing a pedagogical underpinning. A between-subjects experiment was conducted with 40 participants to measure the knowledge transfer of the VR learning environment against the official textbook. In addition, VR's theorized learning benefits of intrinsic motivation, situational interest, and self-efficacy were compared with textbook-based learning. Lastly, the design quality of the learning environment was assessed based on its learning and user experience. We employed a primarily quantitative approach to data collection and analysis, using a combination of knowledge test results and questionnaires, with supporting qualitative data from semi-structured interviews and observation notes to answer our hypotheses. The results found a significant difference between the knowledge transfer of both conditions, with textbook-based learning more effectively transferring factual and conceptual knowledge than VR. No significant difference was found in reported self-efficacy between the two conditions but was found in reported levels of intrinsic motivation and situational interest, which were higher in the VR condition. The design was found to have facilitated a good user and learning experience, assessed via questionnaire responses. During interviews, VR participants reported high levels of satisfaction with the experience, praising the hands-on learning approach and interactivity, while reporting frustration with the lack of knowledge reinforcement and initial difficulties with the controls. A key finding was that presence was found to be negatively associated with knowledge transfer, which we theorize to be caused by the novelty of the realistic VR environment distracting participants from the more familiar lesson content. This research contributes to the body of work related to knowledge transfer within VR in this domain while highlighting key pedagogical and design considerations that can be used to inform future design implementations.
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