Cables exist in a large number of complex electronic devices, the quality of cable process design has a direct impact on the service quality and efficiency of the equipment. Cable process planning is a complex, time-consuming, and typically knowledge-intensive task that involves product information, process routing, parameters, and material selection, depending on the experience and knowledge of the process designers heavily. However, the unstructured and tacit nature of the knowledge makes it difficult to reuse. To implement knowledge-based intelligent cable process reasoning, and increase the design quality of the cable process plan while lowering the cost, it is critical to managing the cable process knowledge systematically and effectively. This paper proposes an ontology-based modeling method for cable product and process knowledge, in this approach, (1) A cable knowledge model containing cable product description and process plan is built; (2) Case-Based Reasoning (CBR) is employed to reuse knowledge from the previous case with the maximum similarity to realize rapid cable process planning, reducing the time and cost of process planning. In addition, a customized control cable process planning is taken as an example to verify the feasibility and effectiveness of the proposed method.
电缆存在于大量复杂的电子设备中，电缆工艺设计的好坏直接影响到设备的使用质量和效率。电缆工艺规划是一项复杂、耗时且典型的知识密集型任务，涉及产品信息、工艺路线、参数和材料选择，这在很大程度上取决于工艺设计者的经验和知识。然而，知识的非结构化和隐性性质使其难以重用。为了实现基于知识的智能电缆工艺推理，在降低成本的同时提高电缆工艺方案的设计质量，对电缆工艺知识进行系统有效的管理至关重要。本文提出了一种基于本体的电缆产品和工艺知识建模方法，该方法:(1)建立了包含电缆产品描述和工艺方案的电缆知识模型;(2)采用基于案例的推理(case - based Reasoning, CBR)，以最大的相似性重用前一个案例中的知识，实现快速的电缆工艺规划，减少工艺规划的时间和成本。并以定制控制电缆工艺规划为例，验证了所提方法的可行性和有效性。
{"title":"An ontology-based modeling and CBR method for cable process planning","authors":"Chen Qiu, Xiaojun Liu, Changbiao Zhu, Feng Xiao","doi":"10.54941/ahfe1003284","DOIUrl":"https://doi.org/10.54941/ahfe1003284","url":null,"abstract":"Cables exist in a large number of complex electronic devices, the quality of cable process design has a direct impact on the service quality and efficiency of the equipment. Cable process planning is a complex, time-consuming, and typically knowledge-intensive task that involves product information, process routing, parameters, and material selection, depending on the experience and knowledge of the process designers heavily. However, the unstructured and tacit nature of the knowledge makes it difficult to reuse. To implement knowledge-based intelligent cable process reasoning, and increase the design quality of the cable process plan while lowering the cost, it is critical to managing the cable process knowledge systematically and effectively. This paper proposes an ontology-based modeling method for cable product and process knowledge, in this approach, (1) A cable knowledge model containing cable product description and process plan is built; (2) Case-Based Reasoning (CBR) is employed to reuse knowledge from the previous case with the maximum similarity to realize rapid cable process planning, reducing the time and cost of process planning. In addition, a customized control cable process planning is taken as an example to verify the feasibility and effectiveness of the proposed method.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"1996 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":"125575916","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 COVID-19 pandemic has caused widespread emotional and psychological impacts globally, leading to feelings of isolation, separation, and disconnection among individuals. In response to this, the present study seeks to explore and document the emotional experience of the COVID-19 pandemic through the creation of an art project titled "All those days in isolation". Using mixed media and collage techniques, the study seeks to create a visual representation of the collective experiences and emotions of a community during the pandemic. This project was inspired by the feelings of isolation and separation that many people have experienced and sought to explore and express these emotions through art. Through a comprehensive review of existing literature and qualitative research, including semi-structured interviews with a group of participants who have experienced the pandemic, this thesis will examine how digital art has been used to record and express emotions.The study found that the COVID-19 pandemic has had a significant impact on mental health and well-being, with high levels of anxiety, stress, and depression reported among individuals who have been directly or indirectly affected by the virus. Additionally, the pandemic has been associated with feelings of loneliness and social isolation, as well as with an increased risk of domestic violence and other forms of abuse.The art project was successful in exploring and expressing the complex emotions of the COVID-19 pandemic, offering a nuanced and well-rounded perspective on the emotional impact of the pandemic on individuals. The study highlights the importance of art in documenting and preserving collective experiences and emotions, as well as its potential to serve as a reflection of society and a tool for coping with stress and traumatic events. Overall, the art project demonstrates the power of art in exploring and expressing complex emotions and providing a space for people to connect with and understand the experiences of others.
{"title":"Artificial Empathy: Exploring the Intersection of Digital Art and Emotional Responses to the COVID-19 Pandemic","authors":"Mingzhu Li, Ming Zhong","doi":"10.54941/ahfe1003272","DOIUrl":"https://doi.org/10.54941/ahfe1003272","url":null,"abstract":"The COVID-19 pandemic has caused widespread emotional and psychological impacts globally, leading to feelings of isolation, separation, and disconnection among individuals. In response to this, the present study seeks to explore and document the emotional experience of the COVID-19 pandemic through the creation of an art project titled \"All those days in isolation\". Using mixed media and collage techniques, the study seeks to create a visual representation of the collective experiences and emotions of a community during the pandemic. This project was inspired by the feelings of isolation and separation that many people have experienced and sought to explore and express these emotions through art. Through a comprehensive review of existing literature and qualitative research, including semi-structured interviews with a group of participants who have experienced the pandemic, this thesis will examine how digital art has been used to record and express emotions.The study found that the COVID-19 pandemic has had a significant impact on mental health and well-being, with high levels of anxiety, stress, and depression reported among individuals who have been directly or indirectly affected by the virus. Additionally, the pandemic has been associated with feelings of loneliness and social isolation, as well as with an increased risk of domestic violence and other forms of abuse.The art project was successful in exploring and expressing the complex emotions of the COVID-19 pandemic, offering a nuanced and well-rounded perspective on the emotional impact of the pandemic on individuals. The study highlights the importance of art in documenting and preserving collective experiences and emotions, as well as its potential to serve as a reflection of society and a tool for coping with stress and traumatic events. Overall, the art project demonstrates the power of art in exploring and expressing complex emotions and providing a space for people to connect with and understand the experiences of others.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"16 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":"129352682","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}
Alperen Sayar, Tuna Çakar, Tunahan Bozkan, Seyit Ertugrul, Mert Güvençli
The recent empirical findings from the related fields including psychology, behavioral sciences, and neuroscience indicate that both emotion and cognition are influential during the decision making processes and so on the final behavioral outcome. On the other hand, emotions are mostly reflected by facial expressions that could be accepted as a vital means of communication and critical for social cognition. This has been known as the facial activation coding in the related academic literature. There have been several different AI-based systems that produce analysis of facial expressions with respect to 7 basic emotions including happy, sad, angry, disgust, fear, surprise, and neutral through the photos captured by camera-based systems. The system we have designed is composed of the following stages: (1) face verification, (2) facial emotion analysis and reporting, (3) emotion recognition from speech. The users upload their online video in which the participants tell about themselves within 3 minutes duration. In this study, several classification methods were applied for model development processes, and the candidates' emotional analysis in online interviews was focused on, and inferences about the situation were attempted using the related face images and sounds. In terms of the face verification system obtained as a result of the model used, 98% success was achieved. The main target of this paper is related to the analysis of facial expressions. The distances between facial landmarks are made up of the starting and ending points of these points. 'Face frames' were obtained while the study was being conducted by extracting human faces from the video using the VideoCapture and Haar Cascade functions in the OpenCV library in the Python programming language with the image taken in the recorded video. The videos consist of 24 frames for 1000 milliseconds. During the whole video, the participant's emotion analysis with respect to facial expressions is provided for the durations of 500 milliseconds. Since there are more than one face in the video, face verification was done with the help of different algorithms: VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib and ArcFace. Emotion analysis via facial landmarks was performed on all photographs of the participant during the interview. DeepFace algorithm was used to analyze face frames through study that recognizes faces using convolutional neural networks, then analyzes age, gender, race, and emotions. The study classified emotions as basic emotions. Emotion analysis was performed on all of the photographs obtained as a result of the verification, and the average mood analysis was carried out throughout the interview, and the data with the highest values on the basis of emotion were also recorded and the probability values have been extracted for further analyses. Besides the local analyses, there have also been global outputs with respect to the whole video session. The main target has been to int
{"title":"Emotional Analysis of Candidates During Online Interviews","authors":"Alperen Sayar, Tuna Çakar, Tunahan Bozkan, Seyit Ertugrul, Mert Güvençli","doi":"10.54941/ahfe1003278","DOIUrl":"https://doi.org/10.54941/ahfe1003278","url":null,"abstract":"The recent empirical findings from the related fields including psychology, behavioral sciences, and neuroscience indicate that both emotion and cognition are influential during the decision making processes and so on the final behavioral outcome. On the other hand, emotions are mostly reflected by facial expressions that could be accepted as a vital means of communication and critical for social cognition. This has been known as the facial activation coding in the related academic literature. There have been several different AI-based systems that produce analysis of facial expressions with respect to 7 basic emotions including happy, sad, angry, disgust, fear, surprise, and neutral through the photos captured by camera-based systems. The system we have designed is composed of the following stages: (1) face verification, (2) facial emotion analysis and reporting, (3) emotion recognition from speech. The users upload their online video in which the participants tell about themselves within 3 minutes duration. In this study, several classification methods were applied for model development processes, and the candidates' emotional analysis in online interviews was focused on, and inferences about the situation were attempted using the related face images and sounds. In terms of the face verification system obtained as a result of the model used, 98% success was achieved. The main target of this paper is related to the analysis of facial expressions. The distances between facial landmarks are made up of the starting and ending points of these points. 'Face frames' were obtained while the study was being conducted by extracting human faces from the video using the VideoCapture and Haar Cascade functions in the OpenCV library in the Python programming language with the image taken in the recorded video. The videos consist of 24 frames for 1000 milliseconds. During the whole video, the participant's emotion analysis with respect to facial expressions is provided for the durations of 500 milliseconds. Since there are more than one face in the video, face verification was done with the help of different algorithms: VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib and ArcFace. Emotion analysis via facial landmarks was performed on all photographs of the participant during the interview. DeepFace algorithm was used to analyze face frames through study that recognizes faces using convolutional neural networks, then analyzes age, gender, race, and emotions. The study classified emotions as basic emotions. Emotion analysis was performed on all of the photographs obtained as a result of the verification, and the average mood analysis was carried out throughout the interview, and the data with the highest values on the basis of emotion were also recorded and the probability values have been extracted for further analyses. Besides the local analyses, there have also been global outputs with respect to the whole video session. The main target has been to int","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":"129459300","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}
M. Vogel, Giuseppe Strina, Christopher Said, Tobias Schmallenbach
Artificial intelligence (AI) in the fourth industrial revolution is a key building block and is becoming more significant as digitization increases.AI implementation in enterprises is increasingly focused on the technological and economic aspects, disregarding the human factors. In this context, the implementation and success of AI technologies depend on employee acceptance. Low employee adoption can lead to poorer performance as well as dissatisfaction. To ensure the expected added value through AI, it is necessary for companies to increase AI acceptance. People see AI as a machine with human intelligence that surpasses employees' capabilities and acts autonomously. Moreover, workers therefore fear that AI will replace humans and that they will lose their jobs in this way. This aspect leads to a distrust of the new technology. This results in a negative attitude towards AI. Since the research field of AI acceptance and its influencing factors have not been sufficiently investigated so far, the aim of this study is to analyze the development of AI acceptance in the industrial environment.In order to achieve the goal of this study, the systematic literature review according to Tranfield et al. (2003) is chosen as the research method, as it draws on previous results and in this way the development of acceptance can be investigated. After discussing the relevance of the topic and the resulting problem, an explanation of the terms that are considered important for the understanding of this study follows. Thereupon the systematic literature research is planned, in which different search terms and databases are determined.In order to analyze the development of the individual aspects, these were then compared with the factors from existing technology acceptance models from earlier years. This provides the insight that the workers without AI experience tend to reject the AI technologies due to the fear of consequences and other factors, therefore, an increase in AI understanding through improved expertise is required. In addition, this work shows that insufficient infrastructure in enterprises slows down AI adoption, which is one of the main problems. Based on the results, a model is established for this purpose, which is compared with the technology acceptance models and the Unified Theory of Acceptance and Use of Technology model to show the similarities and differences of the factors of technology acceptance.
人工智能(AI)是第四次工业革命的关键组成部分，随着数字化的增加，它变得越来越重要。人工智能在企业中的实施越来越侧重于技术和经济方面，忽视了人为因素。在这种情况下，人工智能技术的实施和成功取决于员工的接受程度。低员工采用率会导致较差的绩效和不满。为了确保通过人工智能获得预期的附加值，企业有必要提高人工智能的接受度。人们认为人工智能是一种具有人类智能的机器，它超越了员工的能力，可以自主行动。此外，工人们因此担心人工智能会取代人类，他们会因此失去工作。这方面导致了对新技术的不信任。这导致了对AI的消极态度。由于目前对人工智能接受度的研究领域及其影响因素的研究还不够充分，本研究的目的是分析人工智能接受度在工业环境中的发展情况。为了实现本研究的目标，我们选择了Tranfield et al.(2003)的系统文献综述作为研究方法，因为它借鉴了以往的结果，通过这种方式可以调查接受度的发展。在讨论了主题的相关性和由此产生的问题之后，对被认为对理解本研究很重要的术语进行了解释。在此基础上，规划了系统的文献研究，确定了不同的检索词和数据库。为了分析各个方面的发展，然后将这些因素与早期已有的技术接受模型中的因素进行比较。这表明，由于担心后果和其他因素，没有人工智能经验的工人倾向于拒绝人工智能技术，因此，需要通过提高专业知识来增加对人工智能的理解。此外，这项工作表明，企业基础设施不足会减缓人工智能的采用，这是主要问题之一。在此基础上，建立了技术接受模型，并与技术接受与使用统一理论模型和技术接受模型进行了比较，以显示技术接受因素的异同。
{"title":"The evolution of artificial intelligence adoption in industry","authors":"M. Vogel, Giuseppe Strina, Christopher Said, Tobias Schmallenbach","doi":"10.54941/ahfe1003282","DOIUrl":"https://doi.org/10.54941/ahfe1003282","url":null,"abstract":"Artificial intelligence (AI) in the fourth industrial revolution is a key building block and is becoming more significant as digitization increases.AI implementation in enterprises is increasingly focused on the technological and economic aspects, disregarding the human factors. In this context, the implementation and success of AI technologies depend on employee acceptance. Low employee adoption can lead to poorer performance as well as dissatisfaction. To ensure the expected added value through AI, it is necessary for companies to increase AI acceptance. People see AI as a machine with human intelligence that surpasses employees' capabilities and acts autonomously. Moreover, workers therefore fear that AI will replace humans and that they will lose their jobs in this way. This aspect leads to a distrust of the new technology. This results in a negative attitude towards AI. Since the research field of AI acceptance and its influencing factors have not been sufficiently investigated so far, the aim of this study is to analyze the development of AI acceptance in the industrial environment.In order to achieve the goal of this study, the systematic literature review according to Tranfield et al. (2003) is chosen as the research method, as it draws on previous results and in this way the development of acceptance can be investigated. After discussing the relevance of the topic and the resulting problem, an explanation of the terms that are considered important for the understanding of this study follows. Thereupon the systematic literature research is planned, in which different search terms and databases are determined.In order to analyze the development of the individual aspects, these were then compared with the factors from existing technology acceptance models from earlier years. This provides the insight that the workers without AI experience tend to reject the AI technologies due to the fear of consequences and other factors, therefore, an increase in AI understanding through improved expertise is required. In addition, this work shows that insufficient infrastructure in enterprises slows down AI adoption, which is one of the main problems. Based on the results, a model is established for this purpose, which is compared with the technology acceptance models and the Unified Theory of Acceptance and Use of Technology model to show the similarities and differences of the factors of technology acceptance.","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":"126219160","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}
Diabetic Retinopathy is a public health disease worldwide, which shows that around one percent of the population suffers from this disease. Likewise, another one percent of patients in the population suffer from this disease, but it is not diagnosed. It is estimated that, within three years, millions of people will suffer from this disease. This will increase the percentage of vascular, ophthalmological and neurological complications, which will translate into premature deaths and deterioration in the quality of life of patients. That is why we face a great challenge, which is to predict and detect the signs of diabetic retinopathy at an early stage.For this reason, this paper presents a Machine Learning model focused on the optimization of a classification method using support vector machines for the early prediction of Diabetic Retinopathy. The optimization of the support vector machine consists of adjusting parameters such as: separation margin penalty between support vectors, separation kernel, among others. This method has been trained using an image dataset called Messidor. In this way, the extraction and preprocessing of the data is carried out to carry out a descriptive analysis and obtain the most relevant variables through supervised learning. In this sense, we can see that the most outstanding variables for the risk of diabetic retinopathy are type 1 diabetes and type 2 diabetes.For the evaluation of the proposed method we have used quality measures such as: MAE, MSE, RSME, but the most important are Accuracy, Precision, Recall and F1 for the optimization of classification problems. Therefore, to show the efficacy and effectiveness of the proposed method, we have used a public database, which has allowed us to accurately predict the signs of diabetic retinopathy. Our method has been compared with other relevant methods in classification problems, such as neural networks and genetic algorithms. The support vector machine has proven to be the best for its accuracy.In the state of the art, the works related to Diabetic Retinopathy are presented, as well as the outstanding works with respect to Machine Learning and especially the most outstanding works in Support Vector Machines. We have described the main parameters of the method and also the general process of the algorithm with the description of each step of the analysis model. We have included the values of hyper parameters experienced in the compared methods. In this way we present the best values of the parameters that have generated the best results.Finally, the most relevant results and the corresponding analysis are presented, where the results of the comparison made with the methods of Neural Networks, SVM and Genetic Algorithm will be evidenced. This study gives way to future research related to diabetic retinopathy with the aim of conjecturing the information and thus seeking a better solution.
{"title":"An analysis model for Machine Learning using Support Vector Machine for the prediction of Diabetic Retinopathy","authors":"Remigio Hurtado, Janneth Matute, Juan Boni","doi":"10.54941/ahfe1001450","DOIUrl":"https://doi.org/10.54941/ahfe1001450","url":null,"abstract":"Diabetic Retinopathy is a public health disease worldwide, which shows that around one percent of the population suffers from this disease. Likewise, another one percent of patients in the population suffer from this disease, but it is not diagnosed. It is estimated that, within three years, millions of people will suffer from this disease. This will increase the percentage of vascular, ophthalmological and neurological complications, which will translate into premature deaths and deterioration in the quality of life of patients. That is why we face a great challenge, which is to predict and detect the signs of diabetic retinopathy at an early stage.For this reason, this paper presents a Machine Learning model focused on the optimization of a classification method using support vector machines for the early prediction of Diabetic Retinopathy. The optimization of the support vector machine consists of adjusting parameters such as: separation margin penalty between support vectors, separation kernel, among others. This method has been trained using an image dataset called Messidor. In this way, the extraction and preprocessing of the data is carried out to carry out a descriptive analysis and obtain the most relevant variables through supervised learning. In this sense, we can see that the most outstanding variables for the risk of diabetic retinopathy are type 1 diabetes and type 2 diabetes.For the evaluation of the proposed method we have used quality measures such as: MAE, MSE, RSME, but the most important are Accuracy, Precision, Recall and F1 for the optimization of classification problems. Therefore, to show the efficacy and effectiveness of the proposed method, we have used a public database, which has allowed us to accurately predict the signs of diabetic retinopathy. Our method has been compared with other relevant methods in classification problems, such as neural networks and genetic algorithms. The support vector machine has proven to be the best for its accuracy.In the state of the art, the works related to Diabetic Retinopathy are presented, as well as the outstanding works with respect to Machine Learning and especially the most outstanding works in Support Vector Machines. We have described the main parameters of the method and also the general process of the algorithm with the description of each step of the analysis model. We have included the values of hyper parameters experienced in the compared methods. In this way we present the best values of the parameters that have generated the best results.Finally, the most relevant results and the corresponding analysis are presented, where the results of the comparison made with the methods of Neural Networks, SVM and Genetic Algorithm will be evidenced. This study gives way to future research related to diabetic retinopathy with the aim of conjecturing the information and thus seeking a better solution.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"33 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":"123165974","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}
Andy Bernhardt, T. Strzalkowski, Ning Sa, Ankita Bhaumik, Gregorios A. Katsios
Imageability is a psycholinguistic property of words that indicates how quickly and easily a word evokes a mental image or other sensory experience. Highly imageable words are easier to read and comprehend, and, as a result, their use in communications, such as social media, makes messages more memorable, and, potentially, more impactful and influential. In this paper, we explore the relationship between the imageability of messages in social media and their influence on the target audience. We focus on messages surrounding important public events and approximate the influence of a message by the number of retweets the message receives. First, we propose novel ways to determine an imageability score for a text, utilizing combinations of word-level imageability scores from the MRCPD+ lexicon, as well as word embeddings, image caption data, and word frequency data. Next, we compare these new imageability score functions to a variety of simple baseline functions in correlation between tweet imageability and number of retweets in the domain of the 2017 French Presidential Elections. We find that the imageability score of messages is correlated with the number of retweets in general, and also when normalized for topic and novelty; thus, imageable language is potentially more influential. We consider grouping tweets into imageability score ranges, and find that tweets within higher ranges of imageability scores receive more retweets on average compared to tweets within lower ranges. Lastly, we manually annotate a small number of tweets for imageability and show that our imageability score functions agree well with the human annotators when the agreement between human raters is high.
{"title":"Does Imageable Language Make Your Tweets More Persuasive?","authors":"Andy Bernhardt, T. Strzalkowski, Ning Sa, Ankita Bhaumik, Gregorios A. Katsios","doi":"10.54941/ahfe1003277","DOIUrl":"https://doi.org/10.54941/ahfe1003277","url":null,"abstract":"Imageability is a psycholinguistic property of words that indicates how quickly and easily a word evokes a mental image or other sensory experience. Highly imageable words are easier to read and comprehend, and, as a result, their use in communications, such as social media, makes messages more memorable, and, potentially, more impactful and influential. In this paper, we explore the relationship between the imageability of messages in social media and their influence on the target audience. We focus on messages surrounding important public events and approximate the influence of a message by the number of retweets the message receives. First, we propose novel ways to determine an imageability score for a text, utilizing combinations of word-level imageability scores from the MRCPD+ lexicon, as well as word embeddings, image caption data, and word frequency data. Next, we compare these new imageability score functions to a variety of simple baseline functions in correlation between tweet imageability and number of retweets in the domain of the 2017 French Presidential Elections. We find that the imageability score of messages is correlated with the number of retweets in general, and also when normalized for topic and novelty; thus, imageable language is potentially more influential. We consider grouping tweets into imageability score ranges, and find that tweets within higher ranges of imageability scores receive more retweets on average compared to tweets within lower ranges. Lastly, we manually annotate a small number of tweets for imageability and show that our imageability score functions agree well with the human annotators when the agreement between human raters is high.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"24 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":"132873789","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 introduce the novel concept of predictive management designed to support managers and their teams with achieving their long-term goals by adopting a new and sustainable AI and human-based approach that aims to identify a team's mood during short human-based control cycles. Predictive management helps managers, team leaders and employees to become more aware of the mood of a team and its members’ feelings by using AI, sentiment analysis and emotion detection. This allows managers to identify issues and solve them together with the team during short control cycles and thus maintain a productive workflow, instead of being overwhelmed by them and risking worsening the corporate performance.
{"title":"Using Artificial Intelligence to Improve Human Performance: A Predictive Management Strategy","authors":"Fabrizio Palmas","doi":"10.54941/ahfe1001441","DOIUrl":"https://doi.org/10.54941/ahfe1001441","url":null,"abstract":"In this paper, we introduce the novel concept of predictive management designed to support managers and their teams with achieving their long-term goals by adopting a new and sustainable AI and human-based approach that aims to identify a team's mood during short human-based control cycles. Predictive management helps managers, team leaders and employees to become more aware of the mood of a team and its members’ feelings by using AI, sentiment analysis and emotion detection. This allows managers to identify issues and solve them together with the team during short control cycles and thus maintain a productive workflow, instead of being overwhelmed by them and risking worsening the corporate performance.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"41 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":"116579801","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}
Image captioning extract image features and automatically describe the content of an image in words. Recently image captioning has broken through the application of natural images and is widely used in the arts. It can be applied to art retrieval and management, and it can also automatically provide artistic introductions for the visually impaired. This paper reviews related research in image captioning of artworks, and divides image captioning into three types, including template-based approach, retrieval-based approach, and generative approach. Furthermore, mainstream generative approaches include Encoder-decoder, Transformer, New generation framework, etc. Finally, this paper summarizes the evaluation metrics for image captioning, and looks forward to the application and future development of art image captioning.
{"title":"Image Caption Generation of Arts: Review and Outlook","authors":"Baoying Zheng, Fang Liu","doi":"10.54941/ahfe1003274","DOIUrl":"https://doi.org/10.54941/ahfe1003274","url":null,"abstract":"Image captioning extract image features and automatically describe the content of an image in words. Recently image captioning has broken through the application of natural images and is widely used in the arts. It can be applied to art retrieval and management, and it can also automatically provide artistic introductions for the visually impaired. This paper reviews related research in image captioning of artworks, and divides image captioning into three types, including template-based approach, retrieval-based approach, and generative approach. Furthermore, mainstream generative approaches include Encoder-decoder, Transformer, New generation framework, etc. Finally, this paper summarizes the evaluation metrics for image captioning, and looks forward to the application and future development of art image captioning.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"89 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":"124598791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：