The Impact of a Mixed Reality Display Configuration on User Behavior with a Virtual Human

2010 ◽  
pp. 42-48 ◽  
Author(s):  
Kyle Johnsen ◽  
Diane Beck ◽  
Benjamin Lok
Keyword(s):  
Mixed Reality ◽  
User Behavior ◽  
Virtual Human ◽  
Display Configuration ◽  
The Impact

scholarly journals Evaluating Virtual Human Role-Players for the Practice and Development of Leadership Skills

2021 ◽  
Vol 2 ◽  
Author(s):  
Gonzalo Suárez ◽  
Sungchul Jung ◽  
Robert W. Lindeman
Keyword(s):  
Leadership Training ◽  
User Study ◽  
Mixed Reality ◽  
Leadership Skills ◽  
Training Session ◽  
Virtual Human ◽  
Training Experience ◽  
Significant Difference ◽  
Role Players ◽  
The Impact

This article reports on a study to evaluate the effectiveness of virtual human (VH) role-players as leadership training tools within two computer-generated environments, virtual reality (VR) and mixed reality (MR), compared to a traditional training method, real human (RH) role-players in a real-world (RW) environment. We developed an experimental training platform to assess the three conditions: RH role-players in RW (RH-RW), VH role-players in VR (VH-VR), and VH role-players in MR (VH-MR), during two practice-type opportunities, namely pre-session and post-session. We conducted a user study where 30 participants played the role of leaders in interacting with either RHs or VHs before and after receiving a leadership training session. We then investigated (1) if VH role-players were as effective as RH role-players during pre- and post-sessions, and (2) the impact that the human-type (RH, VH) in conjunction with the environment-type (RW, VR, MR) had on the outcomes. We also collected user reactions and learning data from the overall training experience. The results showed a regular increase in performance from pre- to post-sessions in all three conditions. However, we did not find a significant difference between VHs and RHs. Interestingly, the VH-MR condition had a more significant influence on performance and task engagement compared to the VH-VR and RH-RW conditions. Based on our findings, we conclude that VH role-players can be as effective as RH role-players to support the practice of leadership skills, where VH-MR could be the best method due to its effectiveness.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals Multimodal Mixed Reality Impact on a Hand Guiding Task with a Holographic Cobot

2020 ◽  
Vol 4 (4) ◽  
pp. 78
Author(s):  
Andoni Rivera Pinto ◽  
Johan Kildal ◽  
Elena Lazkano
Keyword(s):  
Augmented Reality ◽  
Industrial Production ◽  
User Study ◽  
Haptic Feedback ◽  
Mixed Reality ◽  
Robot Arm ◽  
Pick And Place ◽  
Place Task ◽  
Guidance Technique ◽  
The Impact

In the context of industrial production, a worker that wants to program a robot using the hand-guidance technique needs that the robot is available to be programmed and not in operation. This means that production with that robot is stopped during that time. A way around this constraint is to perform the same manual guidance steps on a holographic representation of the digital twin of the robot, using augmented reality technologies. However, this presents the limitation of a lack of tangibility of the visual holograms that the user tries to grab. We present an interface in which some of the tangibility is provided through ultrasound-based mid-air haptics actuation. We report a user study that evaluates the impact that the presence of such haptic feedback may have on a pick-and-place task of the wrist of a holographic robot arm which we found to be beneficial.

scholarly journals The Effects of Explicit and Implicit Interaction on User Experiences in a Mixed Reality Installation: The Synthetic Oracle

2009 ◽  
Vol 18 (4) ◽  
pp. 277-285 ◽  
Author(s):  
Sergi Bermúdez i Badia ◽  
Aleksander Valjamae ◽  
Fabio Manzi ◽  
Ulysses Bernardet ◽  
Anna Mura ◽  
...  
Keyword(s):  
Mixed Reality ◽  
User Behavior ◽  
Activity Level ◽  
Online Behavior ◽  
User Experiences ◽  
Motion Data ◽  
Interaction Mode ◽  
Arm Motion ◽  
And Behavior ◽  
Full Body

Virtual and mixed reality environments (VMRE) often imply full-body human-computer interaction scenarios. We used a public multimodal mixed reality installation, the Synthetic Oracle, and a between-groups design to study the effects of implicit (e.g., passively walking) or explicit (e.g., pointing) interaction modes on the users' emotional and engagement experiences, and we assessed it using questionnaires. Additionally, real-time arm motion data was used to categorize the user behavior and to provide interaction possibilities for the explicit interaction group. The results show that the online behavior classification corresponded well to the users' interaction mode. In addition, contrary to the explicit interaction, the engagement ratings from implicit users were positively correlated with a valence but were uncorrelated with arousal ratings. Interestingly, arousal levels were correlated with different behaviors displayed by the visitors depending on the interaction mode. Hence, this study confirms that the activity level and behavior of users modulates their experience, and that in turn, the interaction mode modulates their behavior. Thus, these results show the importance of the selected interaction mode when designing users' experiences in VMRE.

scholarly journals Influence Model of User Behavior Characteristics on Information Dissemination

2016 ◽  
Vol 11 (2) ◽  
pp. 209 ◽  
Author(s):  
Shao Chun Han ◽  
Yun Liu ◽  
Hui Ling Chen ◽  
Zhen Jiang Zhang
Keyword(s):  
Human Behavior ◽  
Information Diffusion ◽  
Statistical Physics ◽  
Information Dissemination ◽  
User Behavior ◽  
Activity Patterns ◽  
Propagation Model ◽  
Propagation Time ◽  
Behavior Characteristics ◽  
The Impact

Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.

scholarly journals INTERNET MARKETING SOLUTIONS TRANSFORMATION DUE TO CHANGES IN CONSUMER'S PERCEPTION OF INFORMATION

2021 ◽  
Author(s):  
Igors Babics
Keyword(s):  
User Behavior ◽  
Online Communication ◽  
Marketing Research ◽  
Modern Society ◽  
Internet Marketing ◽  
Online Marketing ◽  
Scientific Article ◽  
Human Thinking ◽  
Internet User Behavior ◽  
The Impact

We have outlined the main aspects of the modern socio-economic space that have led to transformation not only in the business sector but also in human thinking. We have examined the aspects of the studied problem expressed in modern scientific works and explained the need for further study of changes in the thinking processes of consumers in the field of transformation of applied Internet marketing solutions for the requests of Internet users. We have analyzed the dynamics and trends of changes in Internet user behavior, thereby identifying the key aspects that should be taken into account when companies create online marketing strategies. We have proposed a list of steps to optimize the marketing strategy of the business in line with new realities.The relevance of the study is due to the social processes of modern society resulting in the tendency to transform consumers' thinking. COVID-19 and self-isolation have had an impact on this phenomenon, accelerating the massive changeover to online communication and online shopping.The goal of this article is to describe the results of a study of changes in consumer thinking in connection with the transformation of realities caused by the global pandemic.The scientific novelty of the study lies in highlighting the peculiarities of information perception by modern consumers associated with the global pandemic, and in substantiating the ways of transforming Internet marketing solutions for companies in an altered reality.The theoretical importance of the research lies in a better understanding of the reasons and features of the transformation of information perception by consumers in modern realities, as well as in the analysis of scientific works to study the impact of informatization and computerization on society thinking, which can be used to study this component in marketing research, including in online marketing. This is the practical value of this work.The practical value of the study lies in identifying the features of the transformation of the thinking of modern consumers through visitors to the website of Cita Lieta ltd. at ceanocosmetics.com.Like in any scientific article, this one has its research limitations. The author explores the transformation of consumer thinking change using the data from the website analytics of one company in a particular niche.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

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