Pub Date : 2016-10-01DOI: 10.1080/13614568.2016.1179797
T. Karakus, Ozlem Baydas, Fatma Gunay, Murat Çoban, Y. Goktas
ABSTRACT There are many issues to be considered when designing virtual worlds for educational purposes. In this study, the term orchestration has acquired a new definition as the moderation of problems encountered during the activity of turning a virtual world into an educational setting for winter sports. A development case showed that community plays a key role in both the emergence of challenges and in the determination of their solutions. The implications of this study showed that activity theory was a useful tool for understanding contextual issues. Therefore, instructional designers first developed relevant tools and community-based solutions. This study attempts to use activity theory in a prescriptive way, though it is known as a descriptive theory. Finally, since virtual world projects have many aspects, the variety of challenges and practical solutions presented in this study will provide practitioners with suggestions on how to overcome problems in future.
{"title":"Orchestrating learning during implementation of a 3D virtual world","authors":"T. Karakus, Ozlem Baydas, Fatma Gunay, Murat Çoban, Y. Goktas","doi":"10.1080/13614568.2016.1179797","DOIUrl":"https://doi.org/10.1080/13614568.2016.1179797","url":null,"abstract":"ABSTRACT There are many issues to be considered when designing virtual worlds for educational purposes. In this study, the term orchestration has acquired a new definition as the moderation of problems encountered during the activity of turning a virtual world into an educational setting for winter sports. A development case showed that community plays a key role in both the emergence of challenges and in the determination of their solutions. The implications of this study showed that activity theory was a useful tool for understanding contextual issues. Therefore, instructional designers first developed relevant tools and community-based solutions. This study attempts to use activity theory in a prescriptive way, though it is known as a descriptive theory. Finally, since virtual world projects have many aspects, the variety of challenges and practical solutions presented in this study will provide practitioners with suggestions on how to overcome problems in future.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"303 - 320"},"PeriodicalIF":1.2,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1179797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1080/13614568.2016.1152309
Rodrigo de Lima-Lopes
ABSTRACT Negroponte [1996. Being digital (1st ed.). New York, NY: Vintage Books] discusses the migration television might take from its air-based broadcasting to the digital environment. This paper takes into consideration the exercise in futurology made by Negroponte [1996. Being digital (1st ed.). New York, NY: Vintage Books] as an inspiration to discuss which Internet TV models are currently adopted in Brazil. They are studied in terms of the platforms used and the nature of the channels available. Results show that a number of devices can be used for Internet TV; some channels are redundant, since they are present in more than one context. There are a number of foreign (broadcasting in their own language) and Brazilian channels that seem to be exclusive in each device. Due to the price of some devices, as well as some issues regarding connectivity in Brazil, some platforms seem to lack local production.
{"title":"Some reflections upon Internet TV in the Brazilian context","authors":"Rodrigo de Lima-Lopes","doi":"10.1080/13614568.2016.1152309","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152309","url":null,"abstract":"ABSTRACT Negroponte [1996. Being digital (1st ed.). New York, NY: Vintage Books] discusses the migration television might take from its air-based broadcasting to the digital environment. This paper takes into consideration the exercise in futurology made by Negroponte [1996. Being digital (1st ed.). New York, NY: Vintage Books] as an inspiration to discuss which Internet TV models are currently adopted in Brazil. They are studied in terms of the platforms used and the nature of the channels available. Results show that a number of devices can be used for Internet TV; some channels are redundant, since they are present in more than one context. There are a number of foreign (broadcasting in their own language) and Brazilian channels that seem to be exclusive in each device. Due to the price of some devices, as well as some issues regarding connectivity in Brazil, some platforms seem to lack local production.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"321 - 340"},"PeriodicalIF":1.2,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1152315
Qibo Sun, Lubao Wang, Shangguang Wang, You Ma, Ching-Hsien Hsu
ABSTRACT Quality of Services (QoS) prediction plays an important role in Web service recommendation. Many existing Web service QoS prediction approaches are highly accurate and useful in Internet environments. However, the QoS data of Web service in Mobile Internet are notably more volatile, which makes these approaches fail in making accurate QoS predictions of Web services. In this paper, by weakening the volatility of QoS data, we propose an accurate Web service QoS prediction approach based on the collaborative filtering algorithm. This approach contains three processes, that is, QoS preprocessing, user similarity computing and QoS predicting. We have implemented our proposed approach with an experiment based on real-world and synthetic datasets. The results demonstrate that our approach outperforms other approaches in Mobile Internet.
{"title":"QoS prediction for Web service in Mobile Internet environment","authors":"Qibo Sun, Lubao Wang, Shangguang Wang, You Ma, Ching-Hsien Hsu","doi":"10.1080/13614568.2016.1152315","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152315","url":null,"abstract":"ABSTRACT Quality of Services (QoS) prediction plays an important role in Web service recommendation. Many existing Web service QoS prediction approaches are highly accurate and useful in Internet environments. However, the QoS data of Web service in Mobile Internet are notably more volatile, which makes these approaches fail in making accurate QoS predictions of Web services. In this paper, by weakening the volatility of QoS data, we propose an accurate Web service QoS prediction approach based on the collaborative filtering algorithm. This approach contains three processes, that is, QoS preprocessing, user similarity computing and QoS predicting. We have implemented our proposed approach with an experiment based on real-world and synthetic datasets. The results demonstrate that our approach outperforms other approaches in Mobile Internet.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"207 - 222"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1152316
V. Bhalla, N. Kumar
ABSTRACT In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.
{"title":"An efficient scheme for automatic web pages categorization using the support vector machine","authors":"V. Bhalla, N. Kumar","doi":"10.1080/13614568.2016.1152316","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152316","url":null,"abstract":"ABSTRACT In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"223 - 242"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1152312
Hosun Yoo, O. Kwon, Namyeon Lee
ABSTRACT With advances in robot technology, interest in robotic e-learning systems has increased. In some laboratories, experiments are being conducted with humanoid robots as artificial tutors because of their likeness to humans, the rich possibilities of using this type of media, and the multimodal interaction capabilities of these robots. The robot-assisted learning system, a special type of e-learning system, aims to increase the learner's concentration, pleasure, and learning performance dramatically. However, very few empirical studies have examined the effect on learning performance of incorporating humanoid robot technology into e-learning systems or people's willingness to accept or adopt robot-assisted learning systems. In particular, human likeness, the essential characteristic of humanoid robots as compared with conventional e-learning systems, has not been discussed in a theoretical context. Hence, the purpose of this study is to propose a theoretical model to explain the process of adoption of robot-assisted learning systems. In the proposed model, human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and para-social relationships; these factors are considered as possible determinants of the degree to which human cognition and affection are related to the adoption of robot-assisted learning systems.
{"title":"Human likeness: cognitive and affective factors affecting adoption of robot-assisted learning systems","authors":"Hosun Yoo, O. Kwon, Namyeon Lee","doi":"10.1080/13614568.2016.1152312","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152312","url":null,"abstract":"ABSTRACT With advances in robot technology, interest in robotic e-learning systems has increased. In some laboratories, experiments are being conducted with humanoid robots as artificial tutors because of their likeness to humans, the rich possibilities of using this type of media, and the multimodal interaction capabilities of these robots. The robot-assisted learning system, a special type of e-learning system, aims to increase the learner's concentration, pleasure, and learning performance dramatically. However, very few empirical studies have examined the effect on learning performance of incorporating humanoid robot technology into e-learning systems or people's willingness to accept or adopt robot-assisted learning systems. In particular, human likeness, the essential characteristic of humanoid robots as compared with conventional e-learning systems, has not been discussed in a theoretical context. Hence, the purpose of this study is to propose a theoretical model to explain the process of adoption of robot-assisted learning systems. In the proposed model, human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and para-social relationships; these factors are considered as possible determinants of the degree to which human cognition and affection are related to the adoption of robot-assisted learning systems.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"169 - 188"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1202474
Seungmin Rho, Yu Chen
Nowadays, many people can participate in intelligence production through various processes, such as publications, distributions, and services. In order to recognize, interpret, and process opinions and sentiments over the web, sentiment analysis is emerging as an important issue in human-centric information technology. Paradigms of sentiment analysis such as machineunderstandable web and human-centric web offer a promising and potential solution to mining and analyzing the very large and varied web data. To improve computers’ understanding of human-centric information, relationship analysis between persons with shared common sense information known to everyone is necessary. The seven papers in this Special Issue address some of the challenges inherent in this. The first paper “Human Likeness: Cognitive and Affective Factors Affecting Adoption of Robot-Assisted Learning Systems” by Hosun Yoo, Ohbyung Kwon, and Namyeon Lee proposes an integrated theoretical model, which explains the effect of human likeness of robots on user’s propensity to adopt robot-assisted learning systems. The human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and parasocial relationships. To validate the proposed model, a robot-assisted learning prototype was utilized and survey data were collected from general users. The resulting data were empirically tested, and the test results were provided and analyzed. The second paper entitled “Predicting Personality Traits Related to Consumer Behavior using SNS Analysis” by Baik et al. proposes a method for predicting the four personality traits – (1) Extroversion, (2) Public Self-Consciousness, (3) Desire for Uniqueness, and (4) Self-Esteem – that correlate with buying behaviors in the recent consumer behavior discipline. They also propose another method to analyze user behaviors in a social network service by using user behavior matrix, friendship analysis, and route analysis. In the third paper entitled “An Efficient Scheme for Automatic Web Pages Categorization using Support Vector Machine”, Vinod Kumar Bhalla and Neeraj Kumar proposes a support vector machine (SVM)-based web page categorization, which is based on identification of specific and relevant features of the web pages. They also developed a feature extraction tool based on the HTML-DOM of web page. They evaluated the proposed scheme using SVM kernel as a classification tool in combination with feature extraction and statistical analysis. The fourth paper entitled “Sentiment Classification Technology Based on Markov Logic Networks” by Hui He, Zhigang Li, Chongchong Yao, and Weizhe Zhang presents a crossdomain multi-task text sentiment classification method based on Markov Logic Networks. Through many-to-one knowledge transfer, labeled text sentiment classification knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was imp
如今，许多人可以通过出版、发行、服务等各种过程参与到情报生产中来。为了识别、解释和处理网络上的意见和情感，情感分析正在成为以人为中心的信息技术中的一个重要问题。情感分析的范式，如机器可理解的网络和以人为中心的网络，为挖掘和分析非常庞大和多样的网络数据提供了一个有前途和潜在的解决方案。为了提高计算机对以人为中心的信息的理解，有必要对每个人都知道的共享常识信息进行关系分析。本期特刊的七篇论文探讨了其中固有的一些挑战。Hosun Yoo, Ohbyung Kwon和Namyeon Lee的第一篇论文“人类相似性:影响采用机器人辅助学习系统的认知和情感因素”提出了一个集成的理论模型，该模型解释了机器人的人类相似性对用户采用机器人辅助学习系统倾向的影响。人类的相似性被概念化为媒介丰富性、多模态交互能力和准社会关系的结合。为了验证所提出的模型，利用了机器人辅助学习原型，并从普通用户中收集了调查数据。对所得数据进行了实证检验，并给出了检验结果并进行了分析。第二篇论文题为“使用SNS分析预测与消费者行为相关的人格特征”，由Baik等人提出了一种预测四种人格特征的方法-(1)外向性，(2)公共自我意识，(3)渴望独特性，(4)自尊-在最近的消费者行为学科中与购买行为相关。他们还提出了另一种分析社交网络服务中用户行为的方法，即使用用户行为矩阵、友谊分析和路由分析。在第三篇论文“a Efficient Scheme for Automatic Web Pages Categorization using Support Vector Machine”中，Vinod Kumar Bhalla和Neeraj Kumar提出了一种基于支持向量机(SVM)的网页分类方法，该方法基于对网页的特定和相关特征的识别。他们还开发了一个基于网页HTML-DOM的特征提取工具。他们使用SVM核作为分类工具，结合特征提取和统计分析来评估所提出的方案。第四篇论文《基于马尔可夫逻辑网络的情感分类技术》，作者是何辉、李志刚、姚崇冲、张伟哲，论文提出了一种基于马尔可夫逻辑网络的跨域多任务文本情感分类方法。通过多对一知识转移，将标记文本情感分类知识成功转移到其他领域，提高了文本倾向领域情感分类分析的精度。
{"title":"Human-centric information technology and applications towards web 3.0","authors":"Seungmin Rho, Yu Chen","doi":"10.1080/13614568.2016.1202474","DOIUrl":"https://doi.org/10.1080/13614568.2016.1202474","url":null,"abstract":"Nowadays, many people can participate in intelligence production through various processes, such as publications, distributions, and services. In order to recognize, interpret, and process opinions and sentiments over the web, sentiment analysis is emerging as an important issue in human-centric information technology. Paradigms of sentiment analysis such as machineunderstandable web and human-centric web offer a promising and potential solution to mining and analyzing the very large and varied web data. To improve computers’ understanding of human-centric information, relationship analysis between persons with shared common sense information known to everyone is necessary. The seven papers in this Special Issue address some of the challenges inherent in this. The first paper “Human Likeness: Cognitive and Affective Factors Affecting Adoption of Robot-Assisted Learning Systems” by Hosun Yoo, Ohbyung Kwon, and Namyeon Lee proposes an integrated theoretical model, which explains the effect of human likeness of robots on user’s propensity to adopt robot-assisted learning systems. The human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and parasocial relationships. To validate the proposed model, a robot-assisted learning prototype was utilized and survey data were collected from general users. The resulting data were empirically tested, and the test results were provided and analyzed. The second paper entitled “Predicting Personality Traits Related to Consumer Behavior using SNS Analysis” by Baik et al. proposes a method for predicting the four personality traits – (1) Extroversion, (2) Public Self-Consciousness, (3) Desire for Uniqueness, and (4) Self-Esteem – that correlate with buying behaviors in the recent consumer behavior discipline. They also propose another method to analyze user behaviors in a social network service by using user behavior matrix, friendship analysis, and route analysis. In the third paper entitled “An Efficient Scheme for Automatic Web Pages Categorization using Support Vector Machine”, Vinod Kumar Bhalla and Neeraj Kumar proposes a support vector machine (SVM)-based web page categorization, which is based on identification of specific and relevant features of the web pages. They also developed a feature extraction tool based on the HTML-DOM of web page. They evaluated the proposed scheme using SVM kernel as a classification tool in combination with feature extraction and statistical analysis. The fourth paper entitled “Sentiment Classification Technology Based on Markov Logic Networks” by Hui He, Zhigang Li, Chongchong Yao, and Weizhe Zhang presents a crossdomain multi-task text sentiment classification method based on Markov Logic Networks. Through many-to-one knowledge transfer, labeled text sentiment classification knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was imp","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"167 - 168"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1202474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60345018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1152313
Jongbum Baik, Kangbok Lee, Soowon Lee, Yongbum Kim, Jayoung Choi
ABSTRACT Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the user's buying behavior or preference history, generated from the user's interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumer's buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits—Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem—that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.
{"title":"Predicting personality traits related to consumer behavior using SNS analysis","authors":"Jongbum Baik, Kangbok Lee, Soowon Lee, Yongbum Kim, Jayoung Choi","doi":"10.1080/13614568.2016.1152313","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152313","url":null,"abstract":"ABSTRACT Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the user's buying behavior or preference history, generated from the user's interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumer's buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits—Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem—that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"50 1","pages":"189 - 206"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1152317
Hui He, Zhigang Li, Chongchong Yao, Weizhe Zhang
ABSTRACT With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.
{"title":"Sentiment classification technology based on Markov logic networks","authors":"Hui He, Zhigang Li, Chongchong Yao, Weizhe Zhang","doi":"10.1080/13614568.2016.1152317","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152317","url":null,"abstract":"ABSTRACT With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"243 - 256"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.1080/13614568.2016.1152320
Seulbeom Kim, Dongwann Kang, K. Yoon
ABSTRACT Video content is used extensively in many fields. However, in some fields, video manipulation techniques are required to improve the human-friendliness of such content. In this paper, we propose a method that automatically generates animations in the style of colored paper mosaics, to create human-friendly, artistic imagery. To enhance temporal coherence while maintaining the characteristics of colored paper mosaics, we also propose a particle video-based method that determines coherent locations for tiles in animations. The proposed method generates evenly distributed particles, which are used to produce animated tiles via our tile modeling process.
{"title":"Human-friendly stylization of video content using simulated colored paper mosaics","authors":"Seulbeom Kim, Dongwann Kang, K. Yoon","doi":"10.1080/13614568.2016.1152320","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152320","url":null,"abstract":"ABSTRACT Video content is used extensively in many fields. However, in some fields, video manipulation techniques are required to improve the human-friendliness of such content. In this paper, we propose a method that automatically generates animations in the style of colored paper mosaics, to create human-friendly, artistic imagery. To enhance temporal coherence while maintaining the characteristics of colored paper mosaics, we also propose a particle video-based method that determines coherent locations for tiles in animations. The proposed method generates evenly distributed particles, which are used to produce animated tiles via our tile modeling process.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"270 - 285"},"PeriodicalIF":1.2,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-27DOI: 10.1080/13614568.2016.1152318
Po-Chuan Lin, Bo-Wei Chen, Hangbae Chang
ABSTRACT This study presents a human-centric technique for social video expansion based on semantic processing and graph analysis. The objective is to increase metadata of an online video and to explore related information, thereby facilitating user browsing activities. To analyze the semantic meaning of a video, shots and scenes are firstly extracted from the video on the server side. Subsequently, this study uses annotations along with ConceptNet to establish the underlying framework. Detailed metadata, including visual objects and audio events among the predefined categories, are indexed by using the proposed method. Furthermore, relevant online media associated with each category are also analyzed to enrich the existing content. With the above-mentioned information, users can easily browse and search the content according to the link analysis and its complementary knowledge. Experiments on a video dataset are conducted for evaluation. The results show that our system can achieve satisfactory performance, thereby demonstrating the feasibility of the proposed idea.
{"title":"Concept indexing and expansion for social multimedia websites based on semantic processing and graph analysis","authors":"Po-Chuan Lin, Bo-Wei Chen, Hangbae Chang","doi":"10.1080/13614568.2016.1152318","DOIUrl":"https://doi.org/10.1080/13614568.2016.1152318","url":null,"abstract":"ABSTRACT This study presents a human-centric technique for social video expansion based on semantic processing and graph analysis. The objective is to increase metadata of an online video and to explore related information, thereby facilitating user browsing activities. To analyze the semantic meaning of a video, shots and scenes are firstly extracted from the video on the server side. Subsequently, this study uses annotations along with ConceptNet to establish the underlying framework. Detailed metadata, including visual objects and audio events among the predefined categories, are indexed by using the proposed method. Furthermore, relevant online media associated with each category are also analyzed to enrich the existing content. With the above-mentioned information, users can easily browse and search the content according to the link analysis and its complementary knowledge. Experiments on a video dataset are conducted for evaluation. The results show that our system can achieve satisfactory performance, thereby demonstrating the feasibility of the proposed idea.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"22 1","pages":"257 - 269"},"PeriodicalIF":1.2,"publicationDate":"2016-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2016.1152318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60344933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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