Pub Date : 2022-09-01DOI: 10.4018/978-1-5225-2255-3.CH010
A. Kilani, A. Hamida, H. Hamam
In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.
{"title":"Artificial Intelligence Review","authors":"A. Kilani, A. Hamida, H. Hamam","doi":"10.4018/978-1-5225-2255-3.CH010","DOIUrl":"https://doi.org/10.4018/978-1-5225-2255-3.CH010","url":null,"abstract":"In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91316821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-2255-3.CH677
I. Wiafe
Although persuasive technology continues to impact behavioral and attitudinal interventions, research has demonstrated that existing design approaches are faced with limitations that impede their effective usage. Mostly, they become obsolete, as user needs change during the use of the persuasive application. This research therefore proposes the unified framework for analyzing, designing, and evaluating of persuasive systems (U-FADE). The proposed framework integrates concepts from various existing models to provide a systematic approach that facilitates persuasive design. It addresses the issue of changing needs of users by considering external and internal activities that may promote or impede persuasion before and during implementation.
{"title":"The Role of U-FADE in Selecting Persuasive System Features","authors":"I. Wiafe","doi":"10.4018/978-1-5225-2255-3.CH677","DOIUrl":"https://doi.org/10.4018/978-1-5225-2255-3.CH677","url":null,"abstract":"Although persuasive technology continues to impact behavioral and attitudinal interventions, research has demonstrated that existing design approaches are faced with limitations that impede their effective usage. Mostly, they become obsolete, as user needs change during the use of the persuasive application. This research therefore proposes the unified framework for analyzing, designing, and evaluating of persuasive systems (U-FADE). The proposed framework integrates concepts from various existing models to provide a systematic approach that facilitates persuasive design. It addresses the issue of changing needs of users by considering external and internal activities that may promote or impede persuasion before and during implementation.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"15 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78027904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-2255-3.CH024
Emily C. Bouck, Sara M. Flanagan, Missy D. Cosby
Apps can serve as assistive technology to support students with disabilities. Yet, there is little support for educators in determining the most effective and efficient apps to support students with disabilities. The objectives of this chapter include (1) examining how apps can serve as assistive technology for students with disabilities; (2) discussing the importance of educators not being arbitrary in their decision to select and implement apps to serve as assistive technology, such as relying on reviews, ratings, app lists, app databases, or the inclusion on a categorization on iTunes or Google Play; and (3) presenting options for educators evaluating apps, which can assist educators in making more informed decisions for apps as assistive technology. Although some app evaluation rubrics exist specifically for students with disabilities, there is little research as to the impact of the rubrics on educators selecting apps or the impact on student learning.
{"title":"Apps as Assistive Technology","authors":"Emily C. Bouck, Sara M. Flanagan, Missy D. Cosby","doi":"10.4018/978-1-5225-2255-3.CH024","DOIUrl":"https://doi.org/10.4018/978-1-5225-2255-3.CH024","url":null,"abstract":"Apps can serve as assistive technology to support students with disabilities. Yet, there is little support for educators in determining the most effective and efficient apps to support students with disabilities. The objectives of this chapter include (1) examining how apps can serve as assistive technology for students with disabilities; (2) discussing the importance of educators not being arbitrary in their decision to select and implement apps to serve as assistive technology, such as relying on reviews, ratings, app lists, app databases, or the inclusion on a categorization on iTunes or Google Play; and (3) presenting options for educators evaluating apps, which can assist educators in making more informed decisions for apps as assistive technology. Although some app evaluation rubrics exist specifically for students with disabilities, there is little research as to the impact of the rubrics on educators selecting apps or the impact on student learning.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79360038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-7368-5.ch012
N. M, Sriharsha K. V., V. A.
This chapter presents a novel approach for moving object detection and tracking based on contour extraction and centroid representation (CECR). Firstly, two consecutive frames are read from the video, and they are converted into grayscale. Next, the absolute difference is calculated between them and the result frame is converted into binary by applying gray threshold technique. The binary frame is segmented using contour extraction algorithm. The centroid representation is used for motion tracking. In the second stage of experiment, initially object is detected by using CECR and motion of each track is estimated by Kalman filter. Experimental results show that the proposed method can robustly detect and track the moving object.
{"title":"Moving Object Detection and Tracking Based on the Contour Extraction and Centroid Representation","authors":"N. M, Sriharsha K. V., V. A.","doi":"10.4018/978-1-5225-7368-5.ch012","DOIUrl":"https://doi.org/10.4018/978-1-5225-7368-5.ch012","url":null,"abstract":"This chapter presents a novel approach for moving object detection and tracking based on contour extraction and centroid representation (CECR). Firstly, two consecutive frames are read from the video, and they are converted into grayscale. Next, the absolute difference is calculated between them and the result frame is converted into binary by applying gray threshold technique. The binary frame is segmented using contour extraction algorithm. The centroid representation is used for motion tracking. In the second stage of experiment, initially object is detected by using CECR and motion of each track is estimated by Kalman filter. Experimental results show that the proposed method can robustly detect and track the moving object.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"06 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86466652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-7368-5.ch088
Nolan Hemmatazad
Broadly speaking, social computing encapsulates the idea of making technologies more aware of, and more in alignment with, the social needs of their users. This allows for the introduction of new modes of communication and collaboration among users, the ability to establish and grow communities of various constitutions, as well as for more dynamic and large-scale content creation, dissemination, and evaluation. This chapter serves the ternary purpose of establishing a broad definition of social computing as it stands today and as it is expected to evolve in the near future, providing an overview of the practical applications of social computing, and examining the present and historic research themes that have made an impression on social computing as an area of academic intrigue. The chapter is intended to be accessible to casual readers, practitioners, and academicians alike, with little technical depth and broad focus throughout, for the purpose of establishing an initial acquaintance with the field.
{"title":"Social Computing","authors":"Nolan Hemmatazad","doi":"10.4018/978-1-5225-7368-5.ch088","DOIUrl":"https://doi.org/10.4018/978-1-5225-7368-5.ch088","url":null,"abstract":"Broadly speaking, social computing encapsulates the idea of making technologies more aware of, and more in alignment with, the social needs of their users. This allows for the introduction of new modes of communication and collaboration among users, the ability to establish and grow communities of various constitutions, as well as for more dynamic and large-scale content creation, dissemination, and evaluation. This chapter serves the ternary purpose of establishing a broad definition of social computing as it stands today and as it is expected to evolve in the near future, providing an overview of the practical applications of social computing, and examining the present and historic research themes that have made an impression on social computing as an area of academic intrigue. The chapter is intended to be accessible to casual readers, practitioners, and academicians alike, with little technical depth and broad focus throughout, for the purpose of establishing an initial acquaintance with the field.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"212 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82830731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-7368-5.ch035
G. Marzano
Cyberbullying is a new, alarming, and evil phenomenon closely connected with relational changes that new technologies are causing in contemporary society. It consists in using the internet to harass, threaten, and harm individuals who are the weakest and most vulnerable. Victims of cyberbullying are mightily children and adolescents. In fact, young people are immersed in new digital technologies and use them without knowing their implications. In fact, there isn't the internet for children and the internet for adults. Both adults and children use the same devices, tools, and ways of communicating and interacting.
{"title":"Cyberbullying","authors":"G. Marzano","doi":"10.4018/978-1-5225-7368-5.ch035","DOIUrl":"https://doi.org/10.4018/978-1-5225-7368-5.ch035","url":null,"abstract":"Cyberbullying is a new, alarming, and evil phenomenon closely connected with relational changes that new technologies are causing in contemporary society. It consists in using the internet to harass, threaten, and harm individuals who are the weakest and most vulnerable. Victims of cyberbullying are mightily children and adolescents. In fact, young people are immersed in new digital technologies and use them without knowing their implications. In fact, there isn't the internet for children and the internet for adults. Both adults and children use the same devices, tools, and ways of communicating and interacting.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73649681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-2255-3.CH016
G. Dounias
In this chapter, computational intelligence and its major methodologies are introduced, and then hybrid intelligent systems are defined, and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade is the result of the extensive success of these systems in a wide range of real-world complex problems, but also has to do with the increased capabilities of computational technology. One of the reasons for this success has to do with the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks, genetic algorithms, or other intelligent algorithms and techniques. Each of the partial methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. The chapter includes recent advances and new findings in the area of hybrid computational intelligence.
{"title":"Hybrid Computational Intelligence and the Basic Concepts and Recent Advances","authors":"G. Dounias","doi":"10.4018/978-1-5225-2255-3.CH016","DOIUrl":"https://doi.org/10.4018/978-1-5225-2255-3.CH016","url":null,"abstract":"In this chapter, computational intelligence and its major methodologies are introduced, and then hybrid intelligent systems are defined, and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade is the result of the extensive success of these systems in a wide range of real-world complex problems, but also has to do with the increased capabilities of computational technology. One of the reasons for this success has to do with the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks, genetic algorithms, or other intelligent algorithms and techniques. Each of the partial methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. The chapter includes recent advances and new findings in the area of hybrid computational intelligence.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76339533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-2255-3.CH017
Xenia A. Naidenova
An approach to incremental classification learning is proposed. Classification learning is based on approximation of a given partitioning of objects into disjointed blocks in multivalued space of attributes. Good approximation is defined in the form of good maximally redundant classification test or good formal concept. A concept of classification context is introduced. Four situations of incremental modification of classification context are considered: adding and deleting objects and adding and deleting values of attributes. Algorithms of changing good concepts in these incremental situations are given and proven.
{"title":"Incremental Approach to Classification Learning","authors":"Xenia A. Naidenova","doi":"10.4018/978-1-5225-2255-3.CH017","DOIUrl":"https://doi.org/10.4018/978-1-5225-2255-3.CH017","url":null,"abstract":"An approach to incremental classification learning is proposed. Classification learning is based on approximation of a given partitioning of objects into disjointed blocks in multivalued space of attributes. Good approximation is defined in the form of good maximally redundant classification test or good formal concept. A concept of classification context is introduced. Four situations of incremental modification of classification context are considered: adding and deleting objects and adding and deleting values of attributes. Algorithms of changing good concepts in these incremental situations are given and proven.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83858198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-7368-5.ch013
M. Koleva
Semantic intelligence is exclusive for the intelligent complex systems way of response to an ever-changing environment. Its major distinctive property is autonomous comprehension and creation of information. Another exclusive property of semantic intelligence is autonomous discrimination between true and false statements. The semantic intelligence naturally arises in the setting of concept of boundedness where it commences from highly non-trivial interplay between structural and functional properties of a complex system. The major generic property of that interplay is that it renders the relation between structural and functional properties to be non-recursive. In result, this renders the efficiency of semantic intelligence to be provided by efficiency of hierarchical self-organization of a complex system prior to the efficiency of software and to the speed of hardware.
{"title":"Semantic Intelligence","authors":"M. Koleva","doi":"10.4018/978-1-5225-7368-5.ch013","DOIUrl":"https://doi.org/10.4018/978-1-5225-7368-5.ch013","url":null,"abstract":"Semantic intelligence is exclusive for the intelligent complex systems way of response to an ever-changing environment. Its major distinctive property is autonomous comprehension and creation of information. Another exclusive property of semantic intelligence is autonomous discrimination between true and false statements. The semantic intelligence naturally arises in the setting of concept of boundedness where it commences from highly non-trivial interplay between structural and functional properties of a complex system. The major generic property of that interplay is that it renders the relation between structural and functional properties to be non-recursive. In result, this renders the efficiency of semantic intelligence to be provided by efficiency of hierarchical self-organization of a complex system prior to the efficiency of software and to the speed of hardware.","PeriodicalId":52560,"journal":{"name":"Foundations and Trends in Human-Computer Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82259588","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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