Different Levels Of Realism Research Articles

Exploring Emotional Responses of Design Styles using Electroencephalogram (EEG)

Emotion is a huge factor in creating immersive experience when playing a video game. It can be stimulated by visual design elements. In particular, different levels of realism have an effect over the human feelings. Referred to as the Design Style, it is a spectrum of mimicking the real world ranging from Abstract to Realistic. Between them is known as Stylized Design. While there have been many studies to understand the relationship between Design Styles and emotions, their experimental observations lack quantitative analysis and prone to misinterpretation. Electroencephalogram (EEG) technologies have made the possibilities to explore emotional responses directly from the brain. This research aims to understand the relationship between Design Styles and the emotional responses of the player. To analyse design styles for correlation with emotions, a working framework for machine learning classification using EEG is used. Subjects played video games of similar gameplay but of two different Design Styles – each game represents each end of the Design Styles spectrum. Analysis of the EEG data revealed distinction of emotional activity between Abstract and Realistic games. Higher intensity, larger data distribution, and abruptness of change in emotions are all associated with Abstract Design. Realism, on the other hand, is associated with a more sustained emotional state and therefore a good choice for maintaining a steady mood.

Increasing the realism of on-screen embodied instructors creates more looking but less learning.

Although adding embodied instructors on the screen is considered an effective way to improve online multimedia learning, its effectiveness is still controversial. The level of realism of embodied on-screen instructors may be an influencing factor, but it is unclear how it affects multimedia learning. We explored whether and how embodied on-screen instructors rendered with different levels of realism in multimedia lessons affect learning process and learning outcomes. We recruited 125 college students as participants. Students learned about neural transmission in an online multimedia lesson that included a real human, cartoon human, cartoon animal or no instructor. Students learning with cartoon human or cartoon animal instructors tended to fixate more on the relevant portions of the screen and performed better on retention and transfer tests than no instructor group. The real human group fixated more on the instructor, fixated less on the relevant portion of the screen and performed worse on a retention test in comparison to the cartoon human group. Fixation time on the instructor fully mediated the relationship between instructor realism and retention score. The addition of embodied on-screen instructors can promote multimedia learning, but the promotion effect would be better if the embodied instructor was a cartoon animal or cartoon human rather than a real human. This suggests an important boundary condition in which less realism of on-screen embodied instructors produces better learning processes and outcomes.

How Important are Detailed Hand Motions for Communication for a Virtual Character Through the Lens of Charades?

Detailed hand motions play an important role in face-to-face communication to emphasize points, describe objects, clarify concepts, or replace words altogether. While shared virtual reality (VR) spaces are becoming more popular, these spaces do not, in most cases, capture and display accurate hand motions. In this article, we investigate the consequences of such errors in hand and finger motions on comprehension, character perception, social presence, and user comfort. We conduct three perceptual experiments where participants guess words and movie titles based on motion captured movements. We introduce errors and alterations to the hand movements and apply techniques to synthesize or correct hand motions. We collect data from more than 1000 Amazon Mechanical Turk participants in two large experiments, and conduct a third experiment in VR. As results might differ depending on the virtual character used, we investigate all effects on two virtual characters of different levels of realism. We furthermore investigate the effects of clip length in our experiments. Amongst other results, we show that the absence of finger motion significantly reduces comprehension and negatively affects people’s perception of a virtual character and their social presence. Adding some hand motions, even random ones, does attenuate some of these effects when it comes to the perception of the virtual character or social presence, but it does not necessarily improve comprehension. Slightly inaccurate or erroneous hand motions are sufficient to achieve the same level of comprehension as with accurate hand motions. They might however still affect the viewers’ impression of a character. Finally, jittering hand motions should be avoided as they significantly decrease user comfort.

What is "Where": Physical Reasoning Informs Object Location.

A central puzzle the visual system tries to solve is: "what is where?" While a great deal of research attempts to model object recognition ("what"), a comparatively smaller body of work seeks to model object location ("where"), especially in perceiving everyday objects. How do people locate an object, right now, in front of them? In three experiments collecting over 35,000 judgements on stimuli spanning different levels of realism (line drawings, real images, and crude forms), participants clicked "where" an object is, as if pointing to it. We modeled their responses with eight different methods, including both human response-based models (judgements of physical reasoning, spatial memory, free-response "click anywhere" judgements, and judgements of where people would grab the object), and image-based models (uniform distributions over the image, convex hull, saliency map, and medial axis). Physical reasoning was the best predictor of "where," performing significantly better than even spatial memory and free-response judgements. Our results offer insight into the perception of object locations while also raising interesting questions about the relationship between physical reasoning and visual perception.

The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks

Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.

A Conceptual Framework for 3D Hologram Talking-Head Character Design

In a previous study, we made some new discoveries regarding the use of holograms, particularly as a substitute for classroom instructors. The appropriate hologram tutor character is an important topic to study, considering that the character design as a hologram can significantly influence student learning emotions. In addition, the character design of the hologram tutor is believed to influence the student’s learning experience (LX). Since hologram tutors can have a positive impact on learning sessions, their use should be maintained. However, one of the issues that will hamper the use of hologram tutors in the future is the size of the platform, which is big and challenging to handle. In fact, hologram technology can also utilize the Talking-Head application. This 3D Hologram Talking-head is more manageable due to its smaller size. However, since the 3D Hologram Talking-head only displays the character’s head, the effect of using it on students’ learning emotions remained unclear. Therefore, in the context of this study, we will determine the emotional responses of students to the use of 3D Hologram Talking-Heads with different levels of realism of various characters.

Computational dosimetry in MRI in presence of hip, knee or shoulder implants: do we need accurate surgery models?

Objective. To quantify the effects of different levels of realism in the description of the anatomy around hip, knee or shoulder implants when simulating, numerically, radiofrequency and gradient-induced heating in magnetic resonance imaging. This quantification is needed to define how precise the digital human model modified with the implant should be to get realistic dosimetric assessments. Approach. The analysis is based on a large number of numerical simulations where four ‘levels of realism’ have been adopted in modelling human bodies carrying orthopaedic implants. Main results. Results show that the quantification of the heating due to switched gradient fields does not strictly require a detailed local anatomical description when preparing the digital human model carrying an implant. In this case, a simple overlapping of the implant CAD with the body anatomy is sufficient to provide a quite good and conservative estimation of the heating. On the contrary, the evaluation of the electromagnetic field distribution and heating caused by the radiofrequency field requires an accurate description of the tissues around the prosthesis. Significance. The results of this paper provide hints for selecting the ‘level of realism’ in the definition of the anatomical models with embedded passive implants when performing simulations that should reproduce, as closely as possible, the in vivo scenarios of patients carrying orthopaedic implants.

Systematic Review of Comparative Studies of the Impact of Realism in Immersive Virtual Experiences

The adoption of immersive virtual experiences (IVEs) opened new research lines where the impact of realism is being studied, allowing developers to focus resources on realism factors proven to improve the user experience the most. We analyzed papers that compared different levels of realism and evaluated their impact on user experience. Exploratorily, we also synthesized the realism terms used by authors. From 1,300 initial documents, 79 met the eligibility criteria. Overall, most of the studies reported that higher realism has a positive impact on user experience. These data allow a better understanding of realism in IVEs, guiding future R&D.

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than of the population is known to have been infected in 2020, could have achieved herd immunity against 2021’s variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes.This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

Large eddy simulations of magnetized mergers of neutron stars with neutrinos

Neutron star mergers are very violent events involving extreme physical processes: dynamic, strong-field gravity, large magnetic field, very hot, dense matter, and the copious production of neutrinos. Accurate modeling of such a system and its associated multi-messenger signals, such as gravitational waves, short gamma ray burst, and kilonova, requires the inclusion of all these processes, and is increasingly important in light of advancements in multi-messenger astronomy generally, and in gravitational wave astronomy in particular (such as the development of third-generation detectors). Several general relativistic codes have been incorporating some of these elements with different levels of realism. Here, we extend our code MHDuet, which can perform large eddy simulations of magnetohydrodynamics to help capture the magnetic field amplification during the merger, and to allow for realistic equations of state and neutrino cooling via a leakage scheme. We perform several tests involving isolated and binary neutron stars demonstrating the accuracy of the code.

Generating Images with Physics-Based Rendering for an Industrial Object Detection Task: Realism versus Domain Randomization.

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-world images remains a problem. Therefore, based on a real-world application, we explored the generation of images with physics-based rendering for an industrial object detection task. Setting up the render engine’s environment requires a lot of choices and parameters. One fundamental question is whether to apply the concept of domain randomization or use domain knowledge to try and achieve photorealism. To answer this question, we compared different strategies for setting up lighting, background, object texture, additional foreground objects and bounding box computation in a data-centric approach. We compared the resulting average precision from generated images with different levels of realism and variability. In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection performance. Based on our results, we provide guidelines and an open-source tool for the generation of synthetic images for new industrial applications.

I-GROUP PRESENCE QUESTIONNAIRE: PSYCHOMETRICALLY REVISED ENGLISH VERSION

I-group Presence Questionnaire (IPQ), which is used to evaluate the mediated experience of presence -especially for virtual reality applications- is originally developed in German and translated to several other languages. However, there is not any psychometric study for these translations including English version, except the Portuguese and Persian translations. We evaluated English translation of IPQ with 36 participants through 12 VR sessions with an overall of 432 samples. Using a partial least squares based factor analysis approach, the original 14-item set is trimmed into 11-items in order to achieve better psychometric qualities. In addition, a covariance based confirmatory factor analysis is executed to compare models. Several indices, even the conservative Cronbach’s alpha indicated that the subscales of 11-item version are reliable, but not the 14-item version. Eliminated items did not lead to a decrease in scales’ sensitivity to identify different levels of Realism, Spatial Presence and Involvement for different virtual environments. Although we provided evidence to remove the items which are identically worded and inversely coded that are causing measurement error, we suggest researchers to employ the 14-items but report the results for both 14-item version and 11-item version, until the psychometric qualities of IPQ in English is confirmed with a larger sample of participants.

Numerical Characterisation of a HP Compressor Stage Equipped with a Closed Shrouded Stator Cavity

Abstract In axial compressors, shrouded stator cavity flows are responsible for performance degradation due to their interaction with the power stream. The present paper aims at exploring the possibility of employing a single stage high pressure axial compressor as a test vehicle for cavity flows investigations. In a first step, the robustness of the adopted RANS approach is tested against experimental data on the closed-cavity baseline configuration (i.e. no downstream-to-upstream recirculation). In a second phase, the effect of different hub cavities layouts of different levels of realism is numerically investigated. The focus is set on the representativeness of a closed cavity configuration with injection. The cavity flow topology and impact on the overall performance are considered in the analysis. At its final extent, this paper provides numerical and experimental guidelines for the robust assessment of cavity flows topology and performance effects.

The effects of visual realism and visuospatial abilities on memorizing soccer tactics

Abstract Objectives Dynamic visualizations have become valuable assets in acquiring and improving sports skills. However, their instructional efficiency depends on their design and on learners’ cognitive abilities. Intending to improve learning sessions using these technologies, this study investigates the effects of visual realism and visuospatial abilities (VSA) on the memorization of soccer scenes. Methods Twenty-four soccer beginners first performed a multitask VSA test followed by the memorization and reproduction of three dynamic sequences with different levels of realism. An eye-tracking system was used in the memorization phase to record gaze behavior and identify the involved cognitive processes. Results Findings revealed that increasing visual realism disrupted learners’ memorization performance and visual processing. Moreover, learners with high-VSA were more efficient in memorizing soccer scenes than those with low-VSA and benefited more from reduced visual realism than low-VSA. Learners’ visual processing revealed that high-VSA were more focused on creating a mental representation of the content whereas low-VSA were rather visually guided when memorizing the scene. Conclusions Results imply considering learners’ VSA and adapting presentations’ visual realism to optimize tactical learning among beginners.

Have We Crossed the Uncanny Valley? Understanding Affinity, Trustworthiness, and Preference for Realistic Digital Humans in Immersive Environments

Developers have long strived to create virtual avatars that are more realistic because they are believed to be preferred over less realistic avatars. However, an “uncanny valley” exists in which avatars trigger aversion when they are almost but not quite realistic. We used a field study to investigate whether users had different affinity, trustworthiness, and preferences for avatars with two levels of realism, one that was close to human-realistic and one a cartoon caricature. We observed behavior, conducted one-on-one interviews, and collected survey data from SIGGRAPH conference attendees who either participated in a live discussion session between two avatars in a VR environment, or observed it via 3D VR headsets or on a large screen 2D video display. Eighteen sessions were conducted over four days, with the same person animating the human-realistic avatar and different guests animating the caricature avatars. The guests who interacted with the human-realistic avatar had a positive experience in the VR environment. The observers had positive evaluations of both avatars while acknowledging their different levels of realism. They rated the human-realistic avatar as more trustworthy, had more affinity for it, and preferred it as a virtual agent. Participants who observed the interview through VR headsets had an even stronger affinity for the human-realistic avatar and stronger preferences for it than those who observed via the 2D screen. Effect sizes ranged from medium to large. Our results suggest that it is now possible to cross the uncanny valley with human-realistic avatars rendered in real time.

Vidnesbyrdlitteraturens stille stemmer

The Silent Voices of Witness Literature. Refugee Crisis in Danish Children’s Literature since 2015
 In 2015, Europe experienced the most massive refugee crisis since World War II. This crisis has been reflected in different kinds of art from poetry to picturebooks. In Denmark as well as in other countries, a number of children’s books has been published about the war in Syria and Syrian and other war refugees. These books have a common ground in sharing knowledge about violence, escape and death. Although fleeing is a known topic and the death of a child character is not an unusual event in children’s books (Clement and Jamali), it may be difficult or even controversial to address traumatizing war experiences and death in works for relatively young readers. Very few refugee children are able to tell their stories themselves since they are eithertoo small, displaced in language, traumatized or even dead (Nel). These children’s stories tend to be represented by others (authors and illustrators) who strive to imagine and bear witness to their situation in an artificial language. In this article, three Danish children’s books by widely acknowledged authors and illustrators are chosen as examples of fictional interpretations of refugee children’s experiences. The texts are diverse in genres and target groups and the stories are told with different levels of realism and fantasy, but they are allconnected to the same theme and context. What and how do these contemporary authors and illustrators tell us about refugees and their experiences, and how are they able to represent or bear witness to the experiences of the child victims who are silenced? The theoretical background of this study is Giorgio Agamben’s theory about witness literature and lacunas in language (Agamben; Engdahl ”Philomelas tunge”).

Development and challenges of planning and scheduling for petroleum and petrochemical production

Production planning and scheduling are becoming the core of production management, which support the decision of a petrochemical company. The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production. The optimization problem considered in industry and academic research is of different levels of realism and complexity, thus increasing the gap. Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem. Additionally, modeling the processes, objectives, and constraints and developing the optimization algorithms are significant for industry and research. This paper introduces the perspective of production planning and scheduling from the development viewpoint.

The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.

The manual generation of training data for the semantic segmentation of medical images using deep neural networks is a time-consuming and error-prone task. In this paper, we investigate the effect of different levels of realism on the training of deep neural networks for semantic segmentation of robotic instruments. An interactive virtual-reality environment was developed to generate synthetic images for robot-aided endoscopic surgery. In contrast with earlier works, we use physically based rendering for increased realism. Using a virtual reality simulator that replicates our robotic setup, three synthetic image databases with an increasing level of realism were generated: flat, basic, and realistic (using the physically-based rendering). Each of those databases was used to train 20 instances of a UNet-based semantic-segmentation deep-learning model. The networks trained with only synthetic images were evaluated on the segmentation of 160 endoscopic images of a phantom. The networks were compared using the Dwass-Steel-Critchlow-Fligner nonparametric test. Our results show that the levels of realism increased the mean intersection-over-union (mIoU) of the networks on endoscopic images of a phantom ([Formula: see text]). The median mIoU values were 0.235 for the flat dataset, 0.458 for the basic, and 0.729 for the realistic. All the networks trained with synthetic images outperformed naive classifiers. Moreover, in an ablation study, we show that the mIoU of physically based rendering is superior to texture mapping ([Formula: see text]) of the instrument (0.606), the background (0.685), and the background and instruments combined (0.672). Using physical-based rendering to generate synthetic images is an effective approach to improve the training of neural networks for the semantic segmentation of surgical instruments in endoscopic images. Our results show that this strategy can be an essential step in the broad applicability of deep neural networks in semantic segmentation tasks and help bridge the domain gap in machine learning.

Classification of virtual fitting room technologies in the fashion industry: from the perspective of consumer experience

ABSTRACTVirtual fitting rooms (VFR) bring great opportunities to the fashion industry by enabling consumers to virtually try on products. However, while VFRs have technically been available for a while, they are less utilised because of consumers’ potential concerns of accuracy of the simulation. Moreover, a variety of VFR technologies exist in the marketplace with each using different solutions and with different capabilities, making it harder for retailers to decide on which one to adopt. Therefore, the purpose of this study is to conduct a classification of available VFR technologies from the perspective of consumer experience, particularly cognitive vs. affective experiences. Data for this study were obtained from multiple sources, including academic journals, trade magazines, company websites, and other internet sources. Seven distinctive types of VFRs available in the marketplace were identified, each providing different consumer experiences and satisfaction via their different levels of realism in the simulation process.

Perspective switch and spatial knowledge acquisition: effects of age, mental rotation ability and visuospatial memory capacity on route learning in virtual environments with different levels of realism

ABSTRACTWe report on a study in which we examine if the visual design of virtual environments (VEs) affects visuospatial knowledge acquisition in younger and older adults with varying cognitive abilities in the context of navigational learning, specifically when a perspective switch is involved. Perspective switch between first-person and aerial-views is an important and commonly executed task in navigation; and it is a special case in studying the effects of aging on navigational performance as well, because, reportedly, it is particularly harder for older people. In a controlled experiment, our participants learned a route in first-person view VE, and reproduced what they learned in an aerial-perspective view in immediate and delayed recall stages. To examine the effects of (and interactions between) multiple factors involved in the experiment in relation to the given task, we provide an in-depth investigation of group differences in spatial knowledge acquisition when a perspective switch is required based on age, mental rotation abilities, and visuospatial memory capacity with three VE designs that differ in levels of realism. Our findings based on the recall accuracy of 81 (42 younger, 39 older) participants in sketching tasks demonstrate significant differences across VE types, overall, in favor of our custom-designed VE in this demanding task. Furthermore, we demonstrate that age and visuospatial memory abilities are strong moderating factors, explicitly in this sketching task that requires a perspective switch, irrespective of VE types.