This article proposes a system for data replication and synchronization in mobile devices which is managed offline, allowing data collection in remote locations or deprived of internet connection. In this process there were shortcomings in the convergence and stability of the data, for which a synchronization procedure (web services) is used to assist it. As a result it was obtained that the synchronization between a database hosted in the cloud, a database hosted locally on the mobile device, the compatibility between different programming languages such as Django of Python as server, the deployment of Web Services and C# as client in the consumption of synchronization services is a success, carrying out a synchronization where the integrity of the data is not lost, enabling the connection of the devices in offline mode, performing the corresponding activities, to the time of having an internet connection to upload the data and keep them synchronized.
{"title":"Synchronization procedure for data collection in offline-online sessions","authors":"Andres Viscaino - Quito, L. Serpa-Andrade","doi":"10.54941/ahfe1001461","DOIUrl":"https://doi.org/10.54941/ahfe1001461","url":null,"abstract":"This article proposes a system for data replication and synchronization in mobile devices which is managed offline, allowing data collection in remote locations or deprived of internet connection. In this process there were shortcomings in the convergence and stability of the data, for which a synchronization procedure (web services) is used to assist it. As a result it was obtained that the synchronization between a database hosted in the cloud, a database hosted locally on the mobile device, the compatibility between different programming languages such as Django of Python as server, the deployment of Web Services and C# as client in the consumption of synchronization services is a success, carrying out a synchronization where the integrity of the data is not lost, enabling the connection of the devices in offline mode, performing the corresponding activities, to the time of having an internet connection to upload the data and keep them synchronized.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","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":"130305590","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}
Urban villages are the legacy of China's rapid urban development, those is characterized by high mobility and complex structure, which is endangering the personal safety of residents in urban villages and the surrounding people, affecting the harmony and stability of the communities in urban villages, and not conducive to social and economic development. By taking the distribution of Robbery, Grab and Theft cases as the data source, combining with big data POI information obtained from Gaode, which is the one of the large map service companies in China. Methods such as kernel density analysis, standard deviation ellipsometry and spatial syntax theory are applied in the study, respectively, in order to quantitatively analyze the relationship between the spatial configuration and the environment of crime distribution within urban villages. The results show that: the accessibility, global integration, local integration and connectivity affect the occurrence of Robbery, Grab and Theft and the escape routes of criminals in the village. Different types of POI points and the occurrence of Robbery, Grab and Theft are correlated. This study helps to identify and improve the environmental factors that induce crime, and provides some references on security for the future renovation and construction of public spaces in urban villages in southern Fujian, China.
{"title":"An Analysis of the Spatial Distribution of the Crime in Urban Villages--Taking Dianqian Village,Xiamen as an Example","authors":"Yiting Wu","doi":"10.54941/ahfe1003289","DOIUrl":"https://doi.org/10.54941/ahfe1003289","url":null,"abstract":"Urban villages are the legacy of China's rapid urban development, those is characterized by high mobility and complex structure, which is endangering the personal safety of residents in urban villages and the surrounding people, affecting the harmony and stability of the communities in urban villages, and not conducive to social and economic development. By taking the distribution of Robbery, Grab and Theft cases as the data source, combining with big data POI information obtained from Gaode, which is the one of the large map service companies in China. Methods such as kernel density analysis, standard deviation ellipsometry and spatial syntax theory are applied in the study, respectively, in order to quantitatively analyze the relationship between the spatial configuration and the environment of crime distribution within urban villages. The results show that: the accessibility, global integration, local integration and connectivity affect the occurrence of Robbery, Grab and Theft and the escape routes of criminals in the village. Different types of POI points and the occurrence of Robbery, Grab and Theft are correlated. This study helps to identify and improve the environmental factors that induce crime, and provides some references on security for the future renovation and construction of public spaces in urban villages in southern Fujian, China.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"8 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":"127674220","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}
Rolando Rubilar Torrealba, Karime Chahuán Jiménez, Hanns de la Fuente-Mella
This research presents a methodological analysis that will allow to actively manage the risk of financial assets, through an understandable study and mix of technical differences used by the financial literature. In this way, the research will allow the delivery of precise information on the risk-generating components of the assets studied. The methodology used corresponds to the wavelet decomposition method, combined with the VaR methodology, which as a whole proves to be an efficient way of controlling the financial risk of the investment portfolios used, thus allowing to identify the main risk generating components to which it is applied. investors and fund managers submit.
{"title":"Econometric Modeling for the Management and Decomposition of Financial Risk","authors":"Rolando Rubilar Torrealba, Karime Chahuán Jiménez, Hanns de la Fuente-Mella","doi":"10.54941/ahfe1001444","DOIUrl":"https://doi.org/10.54941/ahfe1001444","url":null,"abstract":"This research presents a methodological analysis that will allow to actively manage the risk of financial assets, through an understandable study and mix of technical differences used by the financial literature. In this way, the research will allow the delivery of precise information on the risk-generating components of the assets studied. The methodology used corresponds to the wavelet decomposition method, combined with the VaR methodology, which as a whole proves to be an efficient way of controlling the financial risk of the investment portfolios used, thus allowing to identify the main risk generating components to which it is applied. investors and fund managers submit.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","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":"128048220","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 the ceramic tile manufacturing industry, the quality of production achieved depends to a large extent on the quality of the tile, which is very important for its classification and price. Currently, this process is performed by human operators, but many industries aim to improve performance and production through automation of this process. In this work, we present the development of a platform based on an artificial vision that allows the identification of defects in ceramic tiles, so that we can classify them according to their quality. The algorithms chosen to develop the platform are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In order to implement these algorithms, the images are preprocessed, the descriptors for defect detection are obtained, then the algorithms are used and the results obtained
{"title":"Development of a platform based on artificial vision with SVM and KNN algorithms for the identification and classification of ceramic tiles","authors":"Edisson Pugo-Mendez, L. Serpa-Andrade","doi":"10.54941/ahfe1001460","DOIUrl":"https://doi.org/10.54941/ahfe1001460","url":null,"abstract":"In the ceramic tile manufacturing industry, the quality of production achieved depends to a large extent on the quality of the tile, which is very important for its classification and price. Currently, this process is performed by human operators, but many industries aim to improve performance and production through automation of this process. In this work, we present the development of a platform based on an artificial vision that allows the identification of defects in ceramic tiles, so that we can classify them according to their quality. The algorithms chosen to develop the platform are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In order to implement these algorithms, the images are preprocessed, the descriptors for defect detection are obtained, then the algorithms are used and the results obtained","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"25 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":"121533304","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}
Daniel N. Cassenti, Aayushi Roy, T. Hawkins, R. Thomson
With unrestrained optimism regarding the possibilities of artificial intelligence (AI) exceeding its actualization, AI developers are under increasing pressure to integrate AI into complex human decision-making tasks without fully understanding the implications of this automation. To investigate how automation may influence human performance in a high workload environment, this study utilizes a triage scenario from intrusion detection using a simulated SNORT interface. Participants classify a series of time-sensitive alerts as real intrusions or false alarms with the assistance of varying levels of automation (LOA) from no automation to fully autonomous. Preliminary results showed that participants tend to prefer and have some performance benefits with intermediate levels of automation.
{"title":"The Effect of Varying Levels of Automation during Initial Triage of Intrusion Detection","authors":"Daniel N. Cassenti, Aayushi Roy, T. Hawkins, R. Thomson","doi":"10.54941/ahfe1001447","DOIUrl":"https://doi.org/10.54941/ahfe1001447","url":null,"abstract":"With unrestrained optimism regarding the possibilities of artificial intelligence (AI) exceeding its actualization, AI developers are under increasing pressure to integrate AI into complex human decision-making tasks without fully understanding the implications of this automation. To investigate how automation may influence human performance in a high workload environment, this study utilizes a triage scenario from intrusion detection using a simulated SNORT interface. Participants classify a series of time-sensitive alerts as real intrusions or false alarms with the assistance of varying levels of automation (LOA) from no automation to fully autonomous. Preliminary results showed that participants tend to prefer and have some performance benefits with intermediate levels of automation.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"26 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":"122438051","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}
Daisy Imbaquingo, M. Ortega-Bustamante, José Jácome, Tatyana K. Saltos-Echeverría, Roger Vaca
In recent years, the use of smartphones in children and adolescents has increased by a considerable number and, therefore, the dangers faced by this population are increasing. Due to this, it is important to develop a technological solution that allows combat this problem by making use of computer vision. Through a bibliographic review, it has been detected those children and adolescents frequently view violent and pornographic images, this allowed us to build a dataset of this type of images to develop an artificial intelligence model. It was successfully developed under the training and validation phases using a google supercomputer (Google Colab), while for the testing phase it was implemented on an android mobile device, using screenshots, images were extracted that the screen projected, and thus later the results were analyzed under statistics using R studio. The computational model detected, with a large percentage of true positives, images and videos of a pornographic and violent nature captured from the screen resolution of a smartphone while the user was using it normally.
{"title":"Detection of inappropriate images on smartphones based on computer vision techniques","authors":"Daisy Imbaquingo, M. Ortega-Bustamante, José Jácome, Tatyana K. Saltos-Echeverría, Roger Vaca","doi":"10.54941/ahfe1001443","DOIUrl":"https://doi.org/10.54941/ahfe1001443","url":null,"abstract":"In recent years, the use of smartphones in children and adolescents has increased by a considerable number and, therefore, the dangers faced by this population are increasing. Due to this, it is important to develop a technological solution that allows combat this problem by making use of computer vision. Through a bibliographic review, it has been detected those children and adolescents frequently view violent and pornographic images, this allowed us to build a dataset of this type of images to develop an artificial intelligence model. It was successfully developed under the training and validation phases using a google supercomputer (Google Colab), while for the testing phase it was implemented on an android mobile device, using screenshots, images were extracted that the screen projected, and thus later the results were analyzed under statistics using R studio. The computational model detected, with a large percentage of true positives, images and videos of a pornographic and violent nature captured from the screen resolution of a smartphone while the user was using it normally.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"119 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":"115879547","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}
Explainable Artificial Intelligence (XAI) has played a significant role in human-computer interaction. The cognitive resources it carries allow humans to understand the complex algorithm powering Artificial Intelligence (AI), virtually resolving the acceptance and adoption barrier from the lack of transparency. This resulted in more systems leveraging XAI and triggering interest and efforts to develop newer and more capable techniques. However, though the research stream is expanding, little is known about the extent of its effectiveness on end-users. Current works have only measured XAI effects on either moment time effect or compared it cross-sectionally on various types of users. Filling this out can improve the understanding of existing studies and provide practical limitations on its use for trust calibration. To address this gap, a multi-time research experiment was conducted with 103 participants to use and evaluate XAI in an image classification application for three days. Measurement that was considered is on perceived usefulness for its cognitive contribution, integral emotions for affective change, trust, and reliance, and was analyzed via covariance-based structural equation modelling. Results showed that time only moderates the path from cognitive to trust and reliance as well as trust to reliance, with its effect dampening through time. On the other hand, affective change has remained consistent in all interactions. This shows that if an AI system uses XAI over a longer time frame, prioritization should be on its affective properties (i.e., things that will trigger emotional change) rather than purely on its cognitive purpose to maximize the positive effect of XAI.
{"title":"Evaluating the Effect of Time on Trust Calibration of Explainable Artificial Intelligence","authors":"Ezekiel Bernardo, R. Seva","doi":"10.54941/ahfe1003280","DOIUrl":"https://doi.org/10.54941/ahfe1003280","url":null,"abstract":"Explainable Artificial Intelligence (XAI) has played a significant role in human-computer interaction. The cognitive resources it carries allow humans to understand the complex algorithm powering Artificial Intelligence (AI), virtually resolving the acceptance and adoption barrier from the lack of transparency. This resulted in more systems leveraging XAI and triggering interest and efforts to develop newer and more capable techniques. However, though the research stream is expanding, little is known about the extent of its effectiveness on end-users. Current works have only measured XAI effects on either moment time effect or compared it cross-sectionally on various types of users. Filling this out can improve the understanding of existing studies and provide practical limitations on its use for trust calibration. To address this gap, a multi-time research experiment was conducted with 103 participants to use and evaluate XAI in an image classification application for three days. Measurement that was considered is on perceived usefulness for its cognitive contribution, integral emotions for affective change, trust, and reliance, and was analyzed via covariance-based structural equation modelling. Results showed that time only moderates the path from cognitive to trust and reliance as well as trust to reliance, with its effect dampening through time. On the other hand, affective change has remained consistent in all interactions. This shows that if an AI system uses XAI over a longer time frame, prioritization should be on its affective properties (i.e., things that will trigger emotional change) rather than purely on its cognitive purpose to maximize the positive effect of XAI.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"25 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":"121563293","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 songbird sings a beautiful melody when there is no ecological need, and the imagination and curiosity are fueled for investigation with biological models of cognitive mechanisms of animal communication. Many animal sensory signals remain a mystery to the logical reasoning of science. Through the evolutionary game theory in ecological cognitive science, predictions are made regarding the signal cost, circumstances, and the individual agent’s state, about which signals (continuous or discrete) should be valued in certain circumstances, but not the details of signal design nor any clue as to why the signals are so diverse in form. In this, investigations have the what, when/where, but not the why. This is reflective of where the debate on robotic consciousness sits. A robot can be programmed to decide to carry out an action in an “if-then” case and use logical algorithms to ensure the calculations can be made to match the possibilities of situations, but to act randomly as an expression of feelings, emotions, passions, or just for the sake of the act, is beyond a calculation. It is the “why” of an existent consciousness, in the “just because” reasoning for the feeling, thought, emotion, passion, or compassion that occurred for the act to come to fruition. A sentient act from emotion or passion may not be a programmable option, as it comes from the identity and free will of the conscious self. The question to be discussed in this paper is whether robots could someday possess a level of consciousness and sentience, to match that of a living human being. This paper will investigate the position that robots will reach a level of sentience and consciousness through the intelligent learning systems of AI. There is strong support for the position that there is a way for electronic networks to become more like human neural networks. The nano and biotechnology grow and the understanding of the human physiology will increase, throughout the smallest of details with neurons, networks, and into the compatibility of neural with electronic systems. AI systems have begun to find support and integration with biotechnology with nanotechnology (West, 2000).
{"title":"The Songbird and the Robotic Self-Awakening","authors":"V. Yerdon","doi":"10.54941/ahfe1001459","DOIUrl":"https://doi.org/10.54941/ahfe1001459","url":null,"abstract":"The songbird sings a beautiful melody when there is no ecological need, and the imagination and curiosity are fueled for investigation with biological models of cognitive mechanisms of animal communication. Many animal sensory signals remain a mystery to the logical reasoning of science. Through the evolutionary game theory in ecological cognitive science, predictions are made regarding the signal cost, circumstances, and the individual agent’s state, about which signals (continuous or discrete) should be valued in certain circumstances, but not the details of signal design nor any clue as to why the signals are so diverse in form. In this, investigations have the what, when/where, but not the why. This is reflective of where the debate on robotic consciousness sits. A robot can be programmed to decide to carry out an action in an “if-then” case and use logical algorithms to ensure the calculations can be made to match the possibilities of situations, but to act randomly as an expression of feelings, emotions, passions, or just for the sake of the act, is beyond a calculation. It is the “why” of an existent consciousness, in the “just because” reasoning for the feeling, thought, emotion, passion, or compassion that occurred for the act to come to fruition. A sentient act from emotion or passion may not be a programmable option, as it comes from the identity and free will of the conscious self. The question to be discussed in this paper is whether robots could someday possess a level of consciousness and sentience, to match that of a living human being. This paper will investigate the position that robots will reach a level of sentience and consciousness through the intelligent learning systems of AI. There is strong support for the position that there is a way for electronic networks to become more like human neural networks. The nano and biotechnology grow and the understanding of the human physiology will increase, throughout the smallest of details with neurons, networks, and into the compatibility of neural with electronic systems. AI systems have begun to find support and integration with biotechnology with nanotechnology (West, 2000).","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"42 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":"133091509","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}
R. Philipsen, P. Brauner, Hannah Biermann, M. Ziefle
With increasing digitization, intelligent software systems are taking over more tasks in everyday human life, both in private and professional contexts. So-called artificial intelligence (AI) ranges from subtle and often unnoticed improvements in daily life, optimizations in data evaluation, assistance systems with which the people interact directly, to perhaps artificial anthropomorphic entities in the future. How-ever, no etiquette yet exists for integrating AI into the human living environment, which has evolved over millennia for human interaction. This paper addresses what roles AI may take, what knowledge AI may have, and how this is influenced by user characteristics. The results show that roles with personal relationships, such as an AI as a friend or partner, are not preferred by users. The higher the confidence in an AI's handling of data, the more likely personal roles are seen as an option for the AI, while the preference for subordinate roles, such as an AI as a servant or a subject, depends on general technology acceptance and belief in a dangerous world. The role attribution is independent from the usage intention and the semantic perception of artificial intelligence, which differs only slightly, e.g., in terms of morality and controllability, from the perception of human intelligence.
{"title":"I Am What I Am – Roles for Artificial Intelligence from the Users’ Perspective","authors":"R. Philipsen, P. Brauner, Hannah Biermann, M. Ziefle","doi":"10.54941/ahfe1001453","DOIUrl":"https://doi.org/10.54941/ahfe1001453","url":null,"abstract":"With increasing digitization, intelligent software systems are taking over more tasks in everyday human life, both in private and professional contexts. So-called artificial intelligence (AI) ranges from subtle and often unnoticed improvements in daily life, optimizations in data evaluation, assistance systems with which the people interact directly, to perhaps artificial anthropomorphic entities in the future. How-ever, no etiquette yet exists for integrating AI into the human living environment, which has evolved over millennia for human interaction. This paper addresses what roles AI may take, what knowledge AI may have, and how this is influenced by user characteristics. The results show that roles with personal relationships, such as an AI as a friend or partner, are not preferred by users. The higher the confidence in an AI's handling of data, the more likely personal roles are seen as an option for the AI, while the preference for subordinate roles, such as an AI as a servant or a subject, depends on general technology acceptance and belief in a dangerous world. The role attribution is independent from the usage intention and the semantic perception of artificial intelligence, which differs only slightly, e.g., in terms of morality and controllability, from the perception of human intelligence.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"65 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":"124438091","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 biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,
{"title":"'\"Human Swarms” of novice sports fans beat professional handicappers when forecasting NFL football games","authors":"Hans Schumann, Louis B. Rosenberg, G. Willcox","doi":"10.54941/ahfe1003287","DOIUrl":"https://doi.org/10.54941/ahfe1003287","url":null,"abstract":"The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"55 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":"125489117","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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