arXiv subjects

Efe Bozkir

Publications and source records attributed to Efe Bozkir.

32 records · Page 2Linked to original sources

arXiv2024

Wrapped in Anansi's Web: Unweaving the Impacts of Generative-AI Personalization and VR Immersion in Oral Storytelling

Oral traditions, vital to cultural identity, are losing relevance among youth due to the dominance of modern media. This study addresses the revitalization of these traditions by reconnecting young people with folklore. We introduce Anansi the Spider VR, a novel virtual space that combines first-person virtual reality (VR) with generative artificial intelligence (Gen-AI)-driven narrative personalization. This space immerses users in the Anansi Spider story, empowering them to influence the narrative as they envision themselves as the `protagonists,' thereby enhancing personal reflection. In a 2 by 2 between-subjects study with 48 participants, we employed a mixed-method approach to measure user engagement and changes in interest, complemented by semi-structured interviews providing qualitative insights into personalization and immersion. Our results indicate that personalization in VR significantly boosts engagement and cultural learning interest. We recommend that future studies using VR and Gen-AI to revitalize oral storytelling prioritize respecting cultural integrity and honoring original storytellers and communities.

cs.HC↗

arXiv2024

Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling

This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game ``Scottish Bonspiel VR''. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage.

cs.HC↗

arXiv2024

Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy

Advances in artificial intelligence and human-computer interaction will likely lead to extended reality (XR) becoming pervasive. While XR can provide users with interactive, engaging, and immersive experiences, non-player characters are often utilized in pre-scripted and conventional ways. This paper argues for using large language models (LLMs) in XR by embedding them in avatars or as narratives to facilitate inclusion through prompt engineering and fine-tuning the LLMs. We argue that this inclusion will promote diversity for XR use. Furthermore, the versatile conversational capabilities of LLMs will likely increase engagement in XR, helping XR become ubiquitous. Lastly, we speculate that combining the information provided to LLM-powered spaces by users and the biometric data obtained might lead to novel privacy invasions. While exploring potential privacy breaches, examining user privacy concerns and preferences is also essential. Therefore, despite challenges, LLM-powered XR is a promising area with several opportunities.

cs.HC↗

arXiv2024

Introduction to Eye Tracking: A Hands-On Tutorial for Students and Practitioners

Eye-tracking technology is widely used in various application areas such as psychology, neuroscience, marketing, and human-computer interaction, as it is a valuable tool for understanding how people process information and interact with their environment. This tutorial provides a comprehensive introduction to eye tracking, from the basics of eye anatomy and physiology to the principles and applications of different eye-tracking systems. The guide is designed to provide a hands-on learning experience for everyone interested in working with eye-tracking technology. Therefore, we include practical case studies to teach students and professionals how to effectively set up and operate an eye-tracking system. The tutorial covers a variety of eye-tracking systems, calibration techniques, data collection, and analysis methods, including fixations, saccades, pupil diameter, and visual scan path analysis. In addition, we emphasize the importance of considering ethical aspects when conducting eye-tracking research and experiments, especially informed consent and participant privacy. We aim to give the reader a solid understanding of basic eye-tracking principles and the practical skills needed to conduct their experiments. Python-based code snippets and illustrative examples are included in the tutorials and can be downloaded at: https://gitlab.lrz.de/hctl/Eye-Tracking-Tutorial.

cs.HC↗

arXiv2024

Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking

As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.

cs.CR↗

arXiv2024

Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT

Classroom observation protocols standardize the assessment of teaching effectiveness and facilitate comprehension of classroom interactions. Whereas these protocols offer teachers specific feedback on their teaching practices, the manual coding by human raters is resource-intensive and often unreliable. This has sparked interest in developing AI-driven, cost-effective methods for automating such holistic coding. Our work explores a multimodal approach to automatically estimating encouragement and warmth in classrooms, a key component of the Global Teaching Insights (GTI) study's observation protocol. To this end, we employed facial and speech emotion recognition with sentiment analysis to extract interpretable features from video, audio, and transcript data. The prediction task involved both classification and regression methods. Additionally, in light of recent large language models' remarkable text annotation capabilities, we evaluated ChatGPT's zero-shot performance on this scoring task based on transcripts. We demonstrated our approach on the GTI dataset, comprising 367 16-minute video segments from 92 authentic lesson recordings. The inferences of GPT-4 and the best-trained model yielded correlations of r = .341 and r = .441 with human ratings, respectively. Combining estimates from both models through averaging, an ensemble approach achieved a correlation of r = .513, comparable to human inter-rater reliability. Our model explanation analysis indicated that text sentiment features were the primary contributors to the trained model's decisions. Moreover, GPT-4 could deliver logical and concrete reasoning as potential teacher guidelines. Our findings provide insights into using advanced, multimodal techniques for automated classroom observation, aiming to foster teacher training through frequent and valuable feedback.

cs.HC↗

arXiv2024

On Task and in Sync: Examining the Relationship between Gaze Synchrony and Self-Reported Attention During Video Lecture Learning

Successful learning depends on learners' ability to sustain attention, which is particularly challenging in online education due to limited teacher interaction. A potential indicator for attention is gaze synchrony, demonstrating predictive power for learning achievements in video-based learning in controlled experiments focusing on manipulating attention. This study (N=84) examines the relationship between gaze synchronization and self-reported attention of learners, using experience sampling, during realistic online video learning. Gaze synchrony was assessed through Kullback-Leibler Divergence of gaze density maps and MultiMatch algorithm scanpath comparisons. Results indicated significantly higher gaze synchronization in attentive participants for both measures and self-reported attention significantly predicted post-test scores. In contrast, synchrony measures did not correlate with learning outcomes. While supporting the hypothesis that attentive learners exhibit similar eye movements, the direct use of synchrony as an attention indicator poses challenges, requiring further research on the interplay of attention, gaze synchrony, and video content type.

cs.HC↗

arXiv2022

User Trust on an Explainable AI-based Medical Diagnosis Support System

Recent research has supported that system explainability improves user trust and willingness to use medical AI for diagnostic support. In this paper, we use chest disease diagnosis based on X-Ray images as a case study to investigate user trust and reliance. Building off explainability, we propose a support system where users (radiologists) can view causal explanations for final decisions. After observing these causal explanations, users provided their opinions of the model predictions and could correct explanations if they did not agree. We measured user trust as the agreement between the model's and the radiologist's diagnosis as well as the radiologists' feedback on the model explanations. Additionally, they reported their trust in the system. We tested our model on the CXR-Eye dataset and it achieved an overall accuracy of 74.1%. However, the experts in our user study agreed with the model for only 46.4% of the cases, indicating the necessity of improving the trust. The self-reported trust score was 3.2 on a scale of 1.0 to 5.0, showing that the users tended to trust the model but the trust still needs to be enhanced.

cs.HC↗

arXiv2022

Towards Everyday Virtual Reality through Eye Tracking

With developments in computer graphics, hardware technology, perception engineering, and human-computer interaction, virtual reality and virtual environments are becoming more integrated into our daily lives. Head-mounted displays, however, are still not used as frequently as other mobile devices such as smart phones and watches. With increased usage of this technology and the acclimation of humans to virtual application scenarios, it is possible that in the near future an everyday virtual reality paradigm will be realized. When considering the marriage of everyday virtual reality and head-mounted displays, eye tracking is an emerging technology that helps to assess human behaviors in a real time and non-intrusive way. Still, multiple aspects need to be researched before these technologies become widely available in daily life. Firstly, attention and cognition models in everyday scenarios should be thoroughly understood. Secondly, as eyes are related to visual biometrics, privacy preserving methodologies are necessary. Lastly, instead of studies or applications utilizing limited human participants with relatively homogeneous characteristics, protocols and use-cases for making such technology more accessible should be essential. In this work, taking the aforementioned points into account, a significant scientific push towards everyday virtual reality has been completed with three main research contributions.

cs.HC↗

arXiv2021

Differential Privacy for Eye Tracking with Temporal Correlations

New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.

cs.CR↗

arXiv2021

Eye Tracking Data Collection Protocol for VR for Remotely Located Subjects using Blockchain and Smart Contracts

Eye tracking data collection in the virtual reality context is typically carried out in laboratory settings, which usually limits the number of participants or consumes at least several months of research time. In addition, under laboratory settings, subjects may not behave naturally due to being recorded in an uncomfortable environment. In this work, we propose a proof-of-concept eye tracking data collection protocol and its implementation to collect eye tracking data from remotely located subjects, particularly for virtual reality using Ethereum blockchain and smart contracts. With the proposed protocol, data collectors can collect high quality eye tracking data from a large number of human subjects with heterogeneous socio-demographic characteristics. The quality and the amount of data can be helpful for various tasks in data-driven human-computer interaction and artificial intelligence.

cs.HC↗

arXiv2021

Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework

Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors. One of the challenges with regard to the social acceptance of eye tracking technology is however the preserving of sensitive and personal information. To tackle this challenge, we employ a privacy-preserving framework based on randomized encoding to train a Support Vector Regression model using synthetic eye images privately to estimate the human gaze. During the computation, none of the parties learn about the data or the result that any other party has. Furthermore, the party that trains the model cannot reconstruct pupil, blinks or visual scanpath. The experimental results show that our privacy-preserving framework is capable of working in real-time, with the same accuracy as compared to non-private version and could be extended to other eye tracking related problems.

cs.CV↗

arXiv2021

Digital Transformations of Classrooms in Virtual Reality

With rapid developments in consumer-level head-mounted displays and computer graphics, immersive VR has the potential to take online and remote learning closer to real-world settings. However, the effects of such digital transformations on learners, particularly for VR, have not been evaluated in depth. This work investigates the interaction-related effects of sitting positions of learners, visualization styles of peer-learners and teachers, and hand-raising behaviors of virtual peer-learners on learners in an immersive VR classroom, using eye tracking data. Our results indicate that learners sitting in the back of the virtual classroom may have difficulties extracting information. Additionally, we find indications that learners engage with lectures more efficiently if virtual avatars are visualized with realistic styles. Lastly, we find different eye movement behaviors towards different performance levels of virtual peer-learners, which should be investigated further. Our findings present an important baseline for design decisions for VR classrooms.

cs.HC↗

arXiv2020

Reinforcement learning for the privacy preservation and manipulation of eye tracking data

In this paper, we present an approach based on reinforcement learning for eye tracking data manipulation. It is based on two opposing agents, where one tries to classify the data correctly and the second agent looks for patterns in the data, which get manipulated to hide specific information. We show that our approach is successfully applicable to preserve the privacy of the subjects. For this purpose, we evaluate our approach iteratively to showcase the behavior of the reinforcement learning based approach. In addition, we evaluate the importance of temporal, as well as spatial, information of eye tracking data for specific classification goals. In the last part of our evaluation, we apply the procedure to further public data sets without re-training the autoencoder or the data manipulator. The results show that the learned manipulation is generalized and applicable to unseen data as well.

cs.LG↗