Saurabh Mittal, Robert L. Wittman, John Gibson, Josh Huffman, Hans Miller
Autonomous and AI-enabled systems present a challenge for integration within the System of Sys-tems (SoS) paradigm. A full system of systems (SoS) testbed is necessary to verify the integrity of a given system and preserve the modularization and accountability of its constituent systems. This integrated system needs to support iterative, continuous testing and development. This need war-rants the development of a virtual environment that provides the ground truth in a simulated sce-nario, interfaces with real-world data, and uses various domain-specific and domain-agnostic simulation systems for development, testing, and evaluation. These required features present a non-trivial challenge wherein constructive models and systems at different levels of fidelity need to interoperate to advance the testing, evaluation, and integration of complex systems. Such a virtual and constructive SoS architecture should be independent of the underlying computational infra-structure but must be cloud-enabled for wider integration of AI-enabled software components. This paper will present a modular Simulation, Experimentation, Analytics, and Test (SEAT) Lay-ered Architecture Framework, a 10-step methodology. This paper will also demonstrate a case study of a hybrid cloud-enabled simulation SoS that allows extensibility, composability, and de-ployability in different target environments.
{"title":"Providing a User Extensible Service-Enabled Multi-Fidelity Hybrid Cloud-Deployable SoS Test and Evaluation (T&E) Infrastructure: Application of Modeling and Simulation (M&S) as a Service (MSaaS)","authors":"Saurabh Mittal, Robert L. Wittman, John Gibson, Josh Huffman, Hans Miller","doi":"10.3390/info14100528","DOIUrl":"https://doi.org/10.3390/info14100528","url":null,"abstract":"Autonomous and AI-enabled systems present a challenge for integration within the System of Sys-tems (SoS) paradigm. A full system of systems (SoS) testbed is necessary to verify the integrity of a given system and preserve the modularization and accountability of its constituent systems. This integrated system needs to support iterative, continuous testing and development. This need war-rants the development of a virtual environment that provides the ground truth in a simulated sce-nario, interfaces with real-world data, and uses various domain-specific and domain-agnostic simulation systems for development, testing, and evaluation. These required features present a non-trivial challenge wherein constructive models and systems at different levels of fidelity need to interoperate to advance the testing, evaluation, and integration of complex systems. Such a virtual and constructive SoS architecture should be independent of the underlying computational infra-structure but must be cloud-enabled for wider integration of AI-enabled software components. This paper will present a modular Simulation, Experimentation, Analytics, and Test (SEAT) Lay-ered Architecture Framework, a 10-step methodology. This paper will also demonstrate a case study of a hybrid cloud-enabled simulation SoS that allows extensibility, composability, and de-ployability in different target environments.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386866","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}
Manav Garg, Pranshav Gajjar, Pooja Shah, Madhu Shukla, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos
The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area.
{"title":"Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation","authors":"Manav Garg, Pranshav Gajjar, Pooja Shah, Madhu Shukla, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos","doi":"10.3390/info14100527","DOIUrl":"https://doi.org/10.3390/info14100527","url":null,"abstract":"The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135344943","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}
Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of Augmented Reality applications in Preschool are captured. Mixed methods with questionnaires and semi-structured interviews were used. The questionnaires record children’s preferences regarding their favorite learning activity between traditional and the Augmented Reality one. Additionally, they explore the activity preschoolers would like to repeat and found most enjoyable: playful. Regarding quantitative data analysis, independent/paired samples t-tests and chi-square test along with bootstrapping with 1000 samples were used. As for the qualitative data collection, educators’ semi-structured interviews focused on three axes: (a) children’s motivation and engagement in Augmented Reality activities, (b) Augmented Reality’s potential to promote skills, and (c) Augmented Reality as a teaching tool in preschool. The emerging results are: Preschoolers prefer more Augmented Reality activities than traditional ones. There are no statistically significant gender differences in preferences for Augmented Reality activities. Educators regard Augmented Reality technology as an innovative, beneficial teaching approach in preschool. However, they express concern regarding the promotion of collaboration among preschoolers via Augmented Reality.
{"title":"Enriching a Traditional Learning Activity in Preschool through Augmented Reality: Children’s and Teachers’ Views","authors":"Sophia Rapti, Theodosios Sapounidis, Sokratis Tselegkaridis","doi":"10.3390/info14100530","DOIUrl":"https://doi.org/10.3390/info14100530","url":null,"abstract":"Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of Augmented Reality applications in Preschool are captured. Mixed methods with questionnaires and semi-structured interviews were used. The questionnaires record children’s preferences regarding their favorite learning activity between traditional and the Augmented Reality one. Additionally, they explore the activity preschoolers would like to repeat and found most enjoyable: playful. Regarding quantitative data analysis, independent/paired samples t-tests and chi-square test along with bootstrapping with 1000 samples were used. As for the qualitative data collection, educators’ semi-structured interviews focused on three axes: (a) children’s motivation and engagement in Augmented Reality activities, (b) Augmented Reality’s potential to promote skills, and (c) Augmented Reality as a teaching tool in preschool. The emerging results are: Preschoolers prefer more Augmented Reality activities than traditional ones. There are no statistically significant gender differences in preferences for Augmented Reality activities. Educators regard Augmented Reality technology as an innovative, beneficial teaching approach in preschool. However, they express concern regarding the promotion of collaboration among preschoolers via Augmented Reality.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343568","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 present a new egocentric metaphor for graph visualization that consists in positioning a graph between two concentric spheres of different radii. It improves the expansion of nodes in space, contrary to 3D spatialization algorithms. The edge drawing is optimized by pushing all the edges into the area delimited by our two concentric spheres so that a user can move freely without being encumbered by edges. Our new metaphor also makes it possible to reduce the display angles in order to have a global view of the graph without leaving the egocentricity.
{"title":"The Spherical Retractable Bubble Space: An Egocentric Graph Visualization throughout a Retractable Visualization Space","authors":"Piriziwè Kobina, Thierry Duval, Laurent Brisson","doi":"10.3390/info14100531","DOIUrl":"https://doi.org/10.3390/info14100531","url":null,"abstract":"In this paper, we present a new egocentric metaphor for graph visualization that consists in positioning a graph between two concentric spheres of different radii. It improves the expansion of nodes in space, contrary to 3D spatialization algorithms. The edge drawing is optimized by pushing all the edges into the area delimited by our two concentric spheres so that a user can move freely without being encumbered by edges. Our new metaphor also makes it possible to reduce the display angles in order to have a global view of the graph without leaving the egocentricity.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135424845","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}
Ali V. Barenji, Benoit Montreuil, Sevda Babalou, Dima Nazzal, Frederick Benaben, Richard DeMillo
Managing the logistics and safety of an election system, from delivering voting machines to the right locations at the right time to ensuring that voting lines remain reasonable in length is a complex problem due to the scarcity of resources, especially human poll workers, and the impact of human behavior and disrupting events on the performance of this critical system. These complexities grew with the need for physical distancing during the COVID-19 pandemic coinciding with multiple key national elections, including the 2020 general presidential election in the USA. In this paper, we propose a digital clone platform leveraging agent-based simulation to model and experiment with resource allocation decisions and voter turnout fluctuations and facilitate “what-if” scenario testing of any election. As a use case, we consider three different concurrent polling location problems, namely, resource allocation, polling layout, and management. The main aim is to reduce voter waiting time and provide visibility of different scenarios for polling and state-level managers. We explain the proposed simulation platform based on Fulton County for the 2020 presidential US election. Fulton County had 238 polling locations in 2020, which provided publicly available voter turnout data. The developed platform realistically models at the county level and at specific locations, suggesting the possible allocation of finite resources among locations in the county and the configuration of each location, accounting for physical, legal, and technical constraints. Multiple realistic scenarios were developed and embedded into the simulation platform to evaluate and verify the different systems. The system performance and key attributes of the election system, such as waiting time, resource utilization, and layout safety, were tested and validated.
{"title":"An Agent-Based Simulation Platform for a Safe Election: From Design to Simulation","authors":"Ali V. Barenji, Benoit Montreuil, Sevda Babalou, Dima Nazzal, Frederick Benaben, Richard DeMillo","doi":"10.3390/info14100529","DOIUrl":"https://doi.org/10.3390/info14100529","url":null,"abstract":"Managing the logistics and safety of an election system, from delivering voting machines to the right locations at the right time to ensuring that voting lines remain reasonable in length is a complex problem due to the scarcity of resources, especially human poll workers, and the impact of human behavior and disrupting events on the performance of this critical system. These complexities grew with the need for physical distancing during the COVID-19 pandemic coinciding with multiple key national elections, including the 2020 general presidential election in the USA. In this paper, we propose a digital clone platform leveraging agent-based simulation to model and experiment with resource allocation decisions and voter turnout fluctuations and facilitate “what-if” scenario testing of any election. As a use case, we consider three different concurrent polling location problems, namely, resource allocation, polling layout, and management. The main aim is to reduce voter waiting time and provide visibility of different scenarios for polling and state-level managers. We explain the proposed simulation platform based on Fulton County for the 2020 presidential US election. Fulton County had 238 polling locations in 2020, which provided publicly available voter turnout data. The developed platform realistically models at the county level and at specific locations, suggesting the possible allocation of finite resources among locations in the county and the configuration of each location, accounting for physical, legal, and technical constraints. Multiple realistic scenarios were developed and embedded into the simulation platform to evaluate and verify the different systems. The system performance and key attributes of the election system, such as waiting time, resource utilization, and layout safety, were tested and validated.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386870","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}
Qingcheng Fan, Sicong Liu, Chunjiang Zhao, Shuqin Li
Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we calculate the linear relationship between each feature and the target variable, resulting in correlation coefficients. Features with high correlation coefficients are selected. Compared to traditional methods, our approach offers two advantages. Firstly, it effectively selects features highly correlated with the target variable from a large feature set, reducing data dimensionality and improving analysis and modeling efficiency. Secondly, our method considers label correlation between features, enhancing the accuracy of selected features and subsequent model performance. Experimental results on three datasets demonstrate the effectiveness of our method in selecting features with high correlation coefficients, leading to superior model performance. Notably, our approach achieves a minimum accuracy improvement of 3.2% for the advanced classifier, lightGBM, surpassing other feature selection methods. In summary, our proposed method, based on instance and label correlation, presents a suitable solution for classification problems.
{"title":"An Instance- and Label-Based Feature Selection Method in Classification Tasks","authors":"Qingcheng Fan, Sicong Liu, Chunjiang Zhao, Shuqin Li","doi":"10.3390/info14100532","DOIUrl":"https://doi.org/10.3390/info14100532","url":null,"abstract":"Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we calculate the linear relationship between each feature and the target variable, resulting in correlation coefficients. Features with high correlation coefficients are selected. Compared to traditional methods, our approach offers two advantages. Firstly, it effectively selects features highly correlated with the target variable from a large feature set, reducing data dimensionality and improving analysis and modeling efficiency. Secondly, our method considers label correlation between features, enhancing the accuracy of selected features and subsequent model performance. Experimental results on three datasets demonstrate the effectiveness of our method in selecting features with high correlation coefficients, leading to superior model performance. Notably, our approach achieves a minimum accuracy improvement of 3.2% for the advanced classifier, lightGBM, surpassing other feature selection methods. In summary, our proposed method, based on instance and label correlation, presents a suitable solution for classification problems.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135426909","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}
The number of Internet of Things (IoT) devices has increased dramatically in recent years, and Bluetooth technology is critical for communication between IoT devices. It is possible to protect electronic communications, the Internet of Things (IoT), and big data from malware and data theft with BlueZ’s Bluetooth File Transfer Filter (BTF). It can use a configurable filter to block unauthorized Bluetooth file transfers. The BTF is available for various Linux distributions and can protect many Bluetooth-enabled devices, including smartphones, tablets, laptops, and the Internet of Things. However, the increased number and density of Bluetooth devices have also created a serious problem—the Bluetooth worm. It poses a severe threat to the security of Bluetooth devices. In this paper, we propose a Bluetooth OBEX Proxy (BOP) to filter malicious files transferred to devices via the OBEX system service in BlueZ. The method described in this article prevents illegal Bluetooth file transfers, defending big data, the Internet of Things (IoT), and electronic communications from malware and data theft. It also protects numerous Bluetooth devices, including smartphones, tablets, laptops, and the Internet of Things, with many Linux distributions. Overall, the detection findings were entirely accurate, with zero false positives and 2.29% misses.
{"title":"Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy","authors":"Fu-Hau Hsu, Min-Hao Wu, Yan-Ling Hwang, Jian-Xin Chen, Jian-Hong Huang, Hao-Jyun Wang, Yi-Wen Lai","doi":"10.3390/info14100525","DOIUrl":"https://doi.org/10.3390/info14100525","url":null,"abstract":"The number of Internet of Things (IoT) devices has increased dramatically in recent years, and Bluetooth technology is critical for communication between IoT devices. It is possible to protect electronic communications, the Internet of Things (IoT), and big data from malware and data theft with BlueZ’s Bluetooth File Transfer Filter (BTF). It can use a configurable filter to block unauthorized Bluetooth file transfers. The BTF is available for various Linux distributions and can protect many Bluetooth-enabled devices, including smartphones, tablets, laptops, and the Internet of Things. However, the increased number and density of Bluetooth devices have also created a serious problem—the Bluetooth worm. It poses a severe threat to the security of Bluetooth devices. In this paper, we propose a Bluetooth OBEX Proxy (BOP) to filter malicious files transferred to devices via the OBEX system service in BlueZ. The method described in this article prevents illegal Bluetooth file transfers, defending big data, the Internet of Things (IoT), and electronic communications from malware and data theft. It also protects numerous Bluetooth devices, including smartphones, tablets, laptops, and the Internet of Things, with many Linux distributions. Overall, the detection findings were entirely accurate, with zero false positives and 2.29% misses.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579599","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}
Qurat Ul Ain, Mohamed Amine Chatti, Komlan Gluck Charles Bakar, Shoeb Joarder, Rawaa Alatrash
Knowledge graphs (KGs) are widely used in the education domain to offer learners a semantic representation of domain concepts from educational content and their relations, termed as educational knowledge graphs (EduKGs). Previous studies on EduKGs have incorporated concept extraction and weighting modules. However, these studies face limitations in terms of accuracy and performance. To address these challenges, this work aims to improve the concept extraction and weighting mechanisms by leveraging state-of-the-art word and sentence embedding techniques. Concretely, we enhance the SIFRank keyphrase extraction method by using SqueezeBERT and we propose a concept-weighting strategy based on SBERT. Furthermore, we conduct extensive experiments on different datasets, demonstrating significant improvements over several state-of-the-art keyphrase extraction and concept-weighting techniques.
{"title":"Automatic Construction of Educational Knowledge Graphs: A Word Embedding-Based Approach","authors":"Qurat Ul Ain, Mohamed Amine Chatti, Komlan Gluck Charles Bakar, Shoeb Joarder, Rawaa Alatrash","doi":"10.3390/info14100526","DOIUrl":"https://doi.org/10.3390/info14100526","url":null,"abstract":"Knowledge graphs (KGs) are widely used in the education domain to offer learners a semantic representation of domain concepts from educational content and their relations, termed as educational knowledge graphs (EduKGs). Previous studies on EduKGs have incorporated concept extraction and weighting modules. However, these studies face limitations in terms of accuracy and performance. To address these challenges, this work aims to improve the concept extraction and weighting mechanisms by leveraging state-of-the-art word and sentence embedding techniques. Concretely, we enhance the SIFRank keyphrase extraction method by using SqueezeBERT and we propose a concept-weighting strategy based on SBERT. Furthermore, we conduct extensive experiments on different datasets, demonstrating significant improvements over several state-of-the-art keyphrase extraction and concept-weighting techniques.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135580101","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}
Mohamed Hesham Ibrahim Abdalla, Simon Malberg, Daryna Dementieva, Edoardo Mosca, Georg Groh
As generative NLP can now produce content nearly indistinguishable from human writing, it is becoming difficult to identify genuine research contributions in academic writing and scientific publications. Moreover, information in machine-generated text can be factually wrong or even entirely fabricated. In this work, we introduce a novel benchmark dataset containing human-written and machine-generated scientific papers from SCIgen, GPT-2, GPT-3, ChatGPT, and Galactica, as well as papers co-created by humans and ChatGPT. We also experiment with several types of classifiers—linguistic-based and transformer-based—for detecting the authorship of scientific text. A strong focus is put on generalization capabilities and explainability to highlight the strengths and weaknesses of these detectors. Our work makes an important step towards creating more robust methods for distinguishing between human-written and machine-generated scientific papers, ultimately ensuring the integrity of scientific literature.
{"title":"A Benchmark Dataset to Distinguish Human-Written and Machine-Generated Scientific Papers","authors":"Mohamed Hesham Ibrahim Abdalla, Simon Malberg, Daryna Dementieva, Edoardo Mosca, Georg Groh","doi":"10.3390/info14100522","DOIUrl":"https://doi.org/10.3390/info14100522","url":null,"abstract":"As generative NLP can now produce content nearly indistinguishable from human writing, it is becoming difficult to identify genuine research contributions in academic writing and scientific publications. Moreover, information in machine-generated text can be factually wrong or even entirely fabricated. In this work, we introduce a novel benchmark dataset containing human-written and machine-generated scientific papers from SCIgen, GPT-2, GPT-3, ChatGPT, and Galactica, as well as papers co-created by humans and ChatGPT. We also experiment with several types of classifiers—linguistic-based and transformer-based—for detecting the authorship of scientific text. A strong focus is put on generalization capabilities and explainability to highlight the strengths and weaknesses of these detectors. Our work makes an important step towards creating more robust methods for distinguishing between human-written and machine-generated scientific papers, ultimately ensuring the integrity of scientific literature.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718806","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}
Lucas Mayr, Lucas Palma, Gustavo Zambonin, Wellington Silvano, Ricardo Custódio
Private key management is a complex obstacle arising from the traditional public key infrastructure model. However, before any related security breach can be addressed, it must first be reliably detected. Certificate Transparency (CT) is an example of a certificate issuance monitoring strategy, developed to detect the possible malfeasance of certification authorities (CAs). To the best of our knowledge, CT and other detection mechanisms do not cover digitally signed documents made by an end user, which are also susceptible to CA misbehavior. We modify the CT framework to handle signed documents via logging certificates in the blockchain to enable the secure and user-friendly monitoring of one-time signatures, backdating protection, and effective CA misbehavior detection. Moreover, to demonstrate the feasibility of our proposal, we present distinct deployment scenarios and analyze the storage, performance, and monetary costs.
{"title":"Monitoring Key Pair Usage through Distributed Ledgers and One-Time Signatures","authors":"Lucas Mayr, Lucas Palma, Gustavo Zambonin, Wellington Silvano, Ricardo Custódio","doi":"10.3390/info14100523","DOIUrl":"https://doi.org/10.3390/info14100523","url":null,"abstract":"Private key management is a complex obstacle arising from the traditional public key infrastructure model. However, before any related security breach can be addressed, it must first be reliably detected. Certificate Transparency (CT) is an example of a certificate issuance monitoring strategy, developed to detect the possible malfeasance of certification authorities (CAs). To the best of our knowledge, CT and other detection mechanisms do not cover digitally signed documents made by an end user, which are also susceptible to CA misbehavior. We modify the CT framework to handle signed documents via logging certificates in the blockchain to enable the secure and user-friendly monitoring of one-time signatures, backdating protection, and effective CA misbehavior detection. Moreover, to demonstrate the feasibility of our proposal, we present distinct deployment scenarios and analyze the storage, performance, and monetary costs.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718925","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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