Delay Sensitivity Classification of Cloud Gaming Content

Cloud Gaming is an emerging service that catches growing interest in the research community as well as industry. While the paradigm shift from a game execution on clients to streaming games from the cloud offers a variety of benefits, the new services also require a highly reliable and low latency network to achieve a satisfying Quality of Experience (QoE) for its users. Using a cloud gaming service with high latency would harm the interaction of the user with the game, leading to a decrease in playing performance and thus frustration of players. However, the negative effect of delay on gaming QoE depends strongly on the game content. At a certain level of delay, a slow-paced card game is typically not as delay sensitive as a shooting game. For optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the impact of the game content. This paper contributes to a better understanding of the delay impact on QoE for cloud gaming applications by identifying game characteristics influencing the delay perception of users. In addition, an expert evaluation methodology to quantify these characteristics, as well as a delay sensitivity classification based on a decision tree is presented. The ratings of 14 experts for the quantification indicated an excellent level of agreement which demonstrates the reliability of the proposed method. Additionally, the decision tree reached an accuracy of 86.6 % on determining the delay sensitivity classes which were derived from a large dataset of subjective input quality ratings during a series of experiments.Comment: Accepted In International Workshop on Immersive Mixed and Virtual Environment Systems 2020. ACM, Istanbul, Turke

Soccer on Social Media

In the era of digitalization, social media has become an integral part of our lives, serving as a significant hub for individuals and businesses to share information, communicate, and engage. This is also the case for professional sports, where leagues, clubs and players are using social media to reach out to their fans. In this respect, a huge amount of time is spent curating multimedia content for various social media platforms and their target users. With the emergence of Artificial Intelligence (AI), AI-based tools for automating content generation and enhancing user experiences on social media have become widely popular. However, to effectively utilize such tools, it is imperative to comprehend the demographics and preferences of users on different platforms, understand how content providers post information in these channels, and how different types of multimedia are consumed by audiences. This report presents an analysis of social media platforms, in terms of demographics, supported multimedia modalities, and distinct features and specifications for different modalities, followed by a comparative case study of select European soccer leagues and teams, in terms of their social media practices. Through this analysis, we demonstrate that social media, while being very important for and widely used by supporters from all ages, also requires a fine-tuned effort on the part of soccer professionals, in order to elevate fan experiences and foster engagement

Enhancing questioning skills through child avatar chatbot training with feedback

Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to trainees. In the iterative process, use of a chatbot avatar to test the language and conversation model is crucial. The model is fine-tuned with interview data and realistic scenarios. This study used a pre-post training design to assess the learning effects on questioning skills across four child interview sessions that involved training with a child avatar chatbot fine-tuned with interview data and realistic scenarios. Thirty university students from the areas of child welfare, social work, and psychology were divided into two groups; one group received direct feedback (n = 12), whereas the other received no feedback (n = 18). An automatic coding function in the language model identified the question types. Information on question types was provided as feedback in the direct feedback group only. The scenario included a 6-year-old girl being interviewed about alleged physical abuse. After the first interview session (baseline), all participants watched a video lecture on memory, witness psychology, and questioning before they conducted two additional interview sessions and completed a post-experience survey. One week later, they conducted a fourth interview and completed another postexperience survey. All chatbot transcripts were coded for interview quality. The language model’s automatic feedback function was found to be highly reliable in classifying question types, reflecting the substantial agreement among the raters [Cohen’s kappa (κ) = 0.80] in coding open-ended, cued recall, and closed questions. Participants who received direct feedback showed a significantly higher improvement in open-ended questioning than those in the non-feedback group, with a significant increase in the number of open-ended questions used between the baseline and each of the other three chat sessions. This study demonstrates that child avatar chatbot training improves interview quality with regard to recommended questioning, especially when combined with direct feedback on questioning

QUALINET white paper on definitions of Immersive Media Experience (IMEx)

With the coming of age of virtual/augmented reality and interactive media, numerous definitions, frameworks, and models of immersion have emerged across different fields ranging from computer graphics to literary works. Immersion is oftentimes used interchangeably with presence as both concepts are closely related. However, there are noticeable interdisciplinary differences regarding definitions, scope, and constituents that are required to be addressed so that a coherent understanding of the concepts can be achieved. Such consensus is vital for paving the directionality of the future of immersive media experiences (IMEx) and all related matters. The aim of this white paper is to provide a survey of definitions of immersion and presence which leads to a definition of immersive media experience (IMEx). The Quality of Experience (QoE) for immersive media is described by establishing a relationship between the concepts of QoE and IMEx followed by application areas of immersive media experience. Influencing factors on immersive media experience are elaborated as well as the assessment of immersive media experience. Finally, standardization activities related to IMEx are highlighted and the white paper is concluded with an outlook related to future developments

Enhancing investigative interview training using a child avatar system: a comparative study of interactive environments

Abstract The impact of investigative interviews by police and Child Protective Services (CPS) on abused children can be profound, making effective training vital. Quality in these interviews often falls short and current training programs are insufficient in enabling adherence to best practice. We present a system for simulating an interactive environment with alleged abuse victims using a child avatar. The purpose of the system is to improve the quality of investigative interviewing by providing a realistic and engaging training experience for police and CPS personnel. We conducted a user study to assess the efficacy of four interactive platforms: VR, 2D desktop, audio, and text chat. CPS workers and child welfare students rated the quality of experience (QoE), realism, responsiveness, immersion, and flow. We also evaluated perceived learning impact, engagement in learning, self-efficacy, and alignment with best practice guidelines. Our findings indicate VR as superior in four out of five quality aspects, with 66% participants favoring it for immersive, realistic training. Quality of questions posed is crucial to these interviews. Distinguishing between appropriate and inappropriate questions, we achieved 87% balanced accuracy in providing effective feedback using our question classification model. Furthermore, CPS professionals demonstrated superior interview quality compared to non-professionals, independent of the platform

Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children

When responding to allegations of child sexual, physical, and psychological abuse, Child Protection Service (CPS) workers and police personnel need to elicit detailed and accurate accounts of the abuse to assist in decision-making and prosecution. Current research emphasizes the importance of the interviewer’s ability to follow empirically based guidelines. In doing so, it is essential to implement economical and scientific training courses for interviewers. Due to recent advances in artificial intelligence, we propose to generate a realistic and interactive child avatar, aiming to mimic a child. Our ongoing research involves the integration and interaction of different components with each other, including how to handle the language, auditory, emotional, and visual components of the avatar. This paper presents three subjective studies that investigate and compare various state-of-the-art methods for implementing multiple aspects of the child avatar. The first user study evaluates the whole system and shows that the system is well received by the expert and highlights the importance of its realism. The second user study investigates the emotional component and how it can be integrated with video and audio, and the third user study investigates realism in the auditory and visual components of the avatar created by different methods. The insights and feedback from these studies have contributed to the refined and improved architecture of the child avatar system which we present here

Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children

When responding to allegations of child sexual, physical, and psychological abuse, Child Protection Service (CPS) workers and police personnel need to elicit detailed and accurate accounts of the abuse to assist in decision-making and prosecution. Current research emphasizes the importance of the interviewer’s ability to follow empirically based guidelines. In doing so, it is essential to implement economical and scientific training courses for interviewers. Due to recent advances in artificial intelligence, we propose to generate a realistic and interactive child avatar, aiming to mimic a child. Our ongoing research involves the integration and interaction of different components with each other, including how to handle the language, auditory, emotional, and visual components of the avatar. This paper presents three subjective studies that investigate and compare various state-of-the-art methods for implementing multiple aspects of the child avatar. The first user study evaluates the whole system and shows that the system is well received by the expert and highlights the importance of its realism. The second user study investigates the emotional component and how it can be integrated with video and audio, and the third user study investigates realism in the auditory and visual components of the avatar created by different methods. The insights and feedback from these studies have contributed to the refined and improved architecture of the child avatar system which we present here

Probing the role of interfacial waters in protein-DNA recognition using a hybrid implicit/explicit solvation model

With the coming of age of virtual/augmented reality and interactive media, numerous definitions, frameworks, and models of immersion have emerged across different fields ranging from computer graphics to literary works. Immersion is oftentimes used interchangeably with presence as both concepts are closely related. However, there are noticeable interdisciplinary differences regarding definitions, scope, and constituents that are required to be addressed so that a coherent understanding of the concepts can be achieved. Such consensus is vital for paving the directionality of the future of immersive media experiences (IMEx) and all related matters. The aim of this white paper is to provide a survey of definitions of immersion and presence which leads to a definition of immersive media experience (IMEx). The Quality of Experience (QoE) for immersive media is described by establishing a relationship between the concepts of QoE and IMEx followed by application areas of immersive media experience. Influencing factors on immersive media experience are elaborated as well as the assessment of immersive media experience. Finally, standardization activities related to IMEx are highlighted and the white paper is concluded with an outlook related to future developments