A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a crypt...A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a cryptocurrency,such digital currencies may be used for many different types of both lawful and illicit activities.The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies.The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations.We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price.In contrast,the frequency of an ethical discussion is positively associated with its price.展开更多
The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for ind...The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.展开更多
Background:Colombia is a Latin American country with a very complex social and political context that has not allowed the allocation of sufficient resources to the fields of science,technology,and innovation(STI).This...Background:Colombia is a Latin American country with a very complex social and political context that has not allowed the allocation of sufficient resources to the fields of science,technology,and innovation(STI).This is particularly worrying for the area of health since not enough resources are allocated for public health,research,or education.Methods:The so-called“Great Survey in Health 2019”was administered online through the SurveyMonkey platform to 5298 people in different regions of the country,representing the public,private,and academic sectors.The questionnaire consisted of 46 open and closed questions,including demographic inquiries.Data analysis involved textual analytics and sentiment analysis.Results:Overall,56%of those surveyed were women within the adult life cycle.Most respondents had a postgraduate education.Greater participation was observed in the Oriental,Bogotá,and Antioquia regions,which also concentrate the largest number of resources for STI.Among the main recommendations derived from the results,priorities include investing in research,personalised medicine,promoting the social appropriation of knowledge,addressing mental health,regulating research through a statute,promoting undergraduate research,and establishing recertification exams to pursue excellence.Conclusion:The results of this original study serve as a fundamental input to promote and strengthen the STI processes in life sciences and health.They serve as a guide to generate public policies and actions that guarantee better health and well-being for the Colombian population,strategically proposing a clear roadmap for the next 20 years.展开更多
Mental disorders negatively affect employee well-being and organizational performance.Organizations face a challenge in terms of how to manage mental health.This paper clarifies three issues(underlying patterns,trends...Mental disorders negatively affect employee well-being and organizational performance.Organizations face a challenge in terms of how to manage mental health.This paper clarifies three issues(underlying patterns,trends,and impact of COVID-19)regarding the scientific study of mental health in organizations from a healthcare analytics framework.The framework comprises eight stages considering a text-driven approach with scientific corpora assisted by linguistic/computational and statistical resources.This study discovers a new taxonomic model comprising five patterns in the scientific discourse on the topic.Trend analyses reveal imbalances and concerns regarding the interests associated with the patterns,which is reinforced by examining patterns“before”and“during”COVID-19.This paper complements psychological/epidemiological studies on mental health in organizations from a healthcare analytics perspective.展开更多
The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corpor...The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corporations,and the government.People are open to sharing opinions,views,and ideas on any topic in different formats out loud.This creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data analytics.This study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data analytics.We explain why social media data are significant elements of the improved data-driven decision-making process.We propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs.We discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to date.We create a taxonomy on social media analytics to meet the need and provide a clear understanding.Tools,techniques,and supporting data type are also discussed in this research work.As a result,researchers will have an easier time deciding which social data analytics would best suit their needs.展开更多
Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartph...Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.展开更多
文摘A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a cryptocurrency,such digital currencies may be used for many different types of both lawful and illicit activities.The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies.The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations.We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price.In contrast,the frequency of an ethical discussion is positively associated with its price.
文摘The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.
基金Special Cooperation Agreement held between the Instituto de Evaluación Tecnológica en Salud(IETS),Colegio Mayor Nuestra Señora del Rosario(UR)and Departamento Administrativo de Ciencia,Tecnología e Innovación(Colciencias),Grant/Award Number:80740-752-2019。
文摘Background:Colombia is a Latin American country with a very complex social and political context that has not allowed the allocation of sufficient resources to the fields of science,technology,and innovation(STI).This is particularly worrying for the area of health since not enough resources are allocated for public health,research,or education.Methods:The so-called“Great Survey in Health 2019”was administered online through the SurveyMonkey platform to 5298 people in different regions of the country,representing the public,private,and academic sectors.The questionnaire consisted of 46 open and closed questions,including demographic inquiries.Data analysis involved textual analytics and sentiment analysis.Results:Overall,56%of those surveyed were women within the adult life cycle.Most respondents had a postgraduate education.Greater participation was observed in the Oriental,Bogotá,and Antioquia regions,which also concentrate the largest number of resources for STI.Among the main recommendations derived from the results,priorities include investing in research,personalised medicine,promoting the social appropriation of knowledge,addressing mental health,regulating research through a statute,promoting undergraduate research,and establishing recertification exams to pursue excellence.Conclusion:The results of this original study serve as a fundamental input to promote and strengthen the STI processes in life sciences and health.They serve as a guide to generate public policies and actions that guarantee better health and well-being for the Colombian population,strategically proposing a clear roadmap for the next 20 years.
基金the Ministry of Science,Technology and Innovation of Colombia(Minciencias).
文摘Mental disorders negatively affect employee well-being and organizational performance.Organizations face a challenge in terms of how to manage mental health.This paper clarifies three issues(underlying patterns,trends,and impact of COVID-19)regarding the scientific study of mental health in organizations from a healthcare analytics framework.The framework comprises eight stages considering a text-driven approach with scientific corpora assisted by linguistic/computational and statistical resources.This study discovers a new taxonomic model comprising five patterns in the scientific discourse on the topic.Trend analyses reveal imbalances and concerns regarding the interests associated with the patterns,which is reinforced by examining patterns“before”and“during”COVID-19.This paper complements psychological/epidemiological studies on mental health in organizations from a healthcare analytics perspective.
文摘The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corporations,and the government.People are open to sharing opinions,views,and ideas on any topic in different formats out loud.This creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data analytics.This study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data analytics.We explain why social media data are significant elements of the improved data-driven decision-making process.We propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs.We discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to date.We create a taxonomy on social media analytics to meet the need and provide a clear understanding.Tools,techniques,and supporting data type are also discussed in this research work.As a result,researchers will have an easier time deciding which social data analytics would best suit their needs.
文摘Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.
