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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Khan, Omar Nam, Hyeongil Kim, Kangsoo University of Calgary Department of Computer Science Canada University of Calgary Department of Electrical and Software Engineering Canada

ISBN: (纸本)9798331514846

As virtual reality (VR) continues to expand, particularly in social VR platforms and immersive gaming, understanding the factors that shape user experience is becoming increasingly important. Avatars and locomotion methods play central roles in influencing how users identify with their virtual representations and navigate virtual spaces. However, little is known about the impact of congruence between these two factors. We conducted a user study with 30 participants, employing two avatar types (human and gorilla) and two locomotion methods (human-like arm-swing and gorilla-like arm-roll), to assess the effects of avatar-locomotion congruence. Our results indicate that congruent avatar-locomotion conditions enhance avatar identification and user experience. © 2025 IEEE.

关键词： Gamification

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Deepfake Detection: Emerging Techniques and Evolving Challenges 12

Deepfake Detection: Emerging Techniques and Evolving Challen...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Natarajan, Krishnaraj Mathur, Abeer Bhargava, Kshitiz Singh, Manvendra Tejpal, Moulik Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387315

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’. Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies. © 2024 IEEE.

关键词： Deep learning

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Designing dataless neural networks for kidney exchange variants

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Neural Computing and Applications 2024年第35期36卷 22265-22275页

作者： Jena, Sangram K. Subramani, K. Velasquez, Alvaro Department of Computer Science and Software Engineering Miami University OxfordOH United States LDCSEE West Virginia University MorgantownWV United States Department of Computer Science University of Colorado Boulder BoulderCO United States

Kidney transplantation is vital for treating end-stage renal disease, impacting roughly one in a thousand Europeans. The search for a suitable deceased donor often leads to prolonged and uncertain wait times, making living donor transplants a viable alternative. However, approximately 40% of living donors are incompatible with their intended recipients. Therefore, many countries have established kidney exchange programs, allowing patients with incompatible donors to participate in "swap" arrangements, exchanging donors with other patients in similar situations. Several variants of the vertex-disjoint cycle cover problem model the above problem, which deals with different aspects of kidney exchange as required. This paper discusses several specific vertex-disjoint cycle cover variants and deals with finding the exact solution. We employ the dataless neural networks framework to establish single differentiable functions for each variant. Recent research highlights the framework’s effectiveness in representing several combinatorial optimization problems. Inspired by these findings, we propose customized dataless neural networks for vertex-disjoint cycle cover variants. We derive a differentiable function for each variant and prove that the function will attain its minimum value if an exact solution is found for the corresponding problem variant. We also provide proof of the correctness of our approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation 21

Smart Glove with Mobile Application to Detect Static Arabic ...

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21st International Learning and Technology Conference, L and T 2024

作者： Elshareif, Mohamed Elgilani Eltahir Ahmad Alias, Nurul Aswani Jomhari, Nazean Sofian, Hazrina University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350393569

The use of assistive technology in the field of education is now a common practice in today's tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise. © 2024 IEEE.

关键词： Assistive technology

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TangibleMoments: Embedding XR Memories onto Physical Objects

TangibleMoments: Embedding XR Memories onto Physical Objects

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Khan, Omar Ahmed, Zaid Nam, Hyeongil Kim, Kangsoo University of Calgary Department of Computer Science Canada Engineering University of Calgary Department of Electrical and Software Canada

ISBN: (纸本)9798331514846

The increasing prevalence of Extended Reality (XR) and head-mounted displays (HMDs), alongside rapid advancements in 3D reality capture technology, unlocks a new paradigm for capturing and reliving past memories/experiences through XR. Current methods for accessing and interacting with these "XR Memories"still lack the ability to fully leverage the range of capabilities afforded by XR and HMDs. We introduce TangibleMoments, a novel framework that enables users to embed XR memories onto physical objects, transforming those objects into "Moments"- tangible user interfaces for accessing and interacting with XR memories. We describe and illustrate five interaction methods as part of this framework: Creating Moments, Recalling Moments, Sharing Moments, Copying Moments, and Clearing Moments. We showcase an initial prototype and discuss possible extensions. © 2025 IEEE.

关键词： Mixed reality

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Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach 9th

Lake Water Level Forecasting Using LSTM and GRU: A Deep Lear...

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9th Future Technologies Conference, FTC 2024

作者： Du, Yuxin Fan, Jing Happonen, Ari Paulraj, Dassan Tuape, Micheal Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Mukkulankatu 19 Lahti15210 Finland Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Yliopistonkatu 34 Lappeenranta53850 Finland Department of Computer Science and Engineering R.M.K. Engineering College Chennai India

ISBN: (纸本)9783031731242

Artificial Intelligence and Deep Learning-based methods show constant promise in addressing time series forecasting challenges. Lake water level forecasting is an essential & significant environmental and societal impact related time series forecasting problem, with climate change connections. Lakes’ diverse hydrological characteristics make high-performance forecasting models to be lake-specific. Thus, comprehensive experiments are necessary to develop accurate deep learning-based forecasting models. We propose an approach for effective and efficient systematic search conduction for high-performance Deep Learning forecasting models. The method is applicable across various time series forecasting challenges. The research was structured around three experimental groups, each focusing on predicting the water levels of Lake Vesijärvi in Lahti, Finland, over periods of 1 day, 3 days, and 7 days, respectively with Long Short-Term Memory and Gated Recurrent Unit. The results are highly promising. All models achieved a Nash-Sutcliffe Efficiency above 0.95 and a Root Mean Squared Error below 0.025. The best-performing model achieved a Nash-Sutcliffe Efficiency above 0.99 and a Root Mean Squared Error below 0.0011. All evaluation metrics were calculated from testing data without signs of overfitting. This research provides insights into Deep Learning-based time series forecasting and a replicable method to conduct such studies effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lakes

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Deepfakes Catcher: A Novel Fused Truncated DenseNet Model for Deepfakes Detection 16th

Deepfakes Catcher: A Novel Fused Truncated DenseNet Model fo...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Khalid, Fatima Javed, Ali Irtaza, Aun Malik, Khalid Mahmood Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Software Engineering University of Engineering and Technology Taxila Pakistan Department of Computer Science and Engineering Oakland University RochesterMI United States

ISBN: (纸本)9789811993305

In recent years, we have witnessed a tremendous evolution in generative adversarial networks resulting in the creation of much realistic fake multimedia content termed deepfakes. The deepfakes are created by superimposing one person’s real facial features, expressions, or lip movements onto another one. Apart from the benefits of deepfakes, it has been largely misused to propagate disinformation about influential persons like celebrities, politicians, etc. Since the deepfakes are created using different generative algorithms and involve much realism, thus it is a challenging task to detect them. Existing deepfakes detection methods have shown lower performance on forged videos that are generated using different algorithms, as well as videos that are of low resolution, compressed, or computationally more complex. To counter these issues, we propose a novel fused truncated DenseNet121 model for deepfakes videos detection. We employ transfer learning to reduce the resources and improve effectiveness, truncation to reduce the parameters and model size, and feature fusion to strengthen the representation by capturing more distinct traits of the input video. Our fused truncated DenseNet model lowers the DenseNet121 parameters count from 8.5 to 0.5 million. This makes our model more effective and lightweight that can be deployed in portable devices for real-time deepfakes detection. Our proposed model can reliably detect various types of deepfakes as well as deepfakes of different generative methods. We evaluated our model on two diverse datasets: a large-scale FaceForensics (FF)++ dataset and the World Leaders (WL) dataset. Our model achieves a remarkable accuracy of 99.03% on the WL dataset and 87.76% on the FF++ which shows the effectiveness of our method for deepfakes detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Developing and Evaluating a Model-Based Metric for Legal Question Answering Systems

Developing and Evaluating a Model-Based Metric for Legal Que...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Bakir, Dilan Yildiz, Beytullah Aktas, Mehmet S. Yildiz Technical University Computer Engineering Department Istanbul Turkey Atilim University Software Engineering Department Ankara Turkey

ISBN: (纸本)9798350324457

In the complicated world of legal law, Question Answering (QA) systems only work if they can give correct, situation-aware, and logically sound answers. Traditional evaluation methods, which rely on superficial similarity measures, can't catch the complex accuracy and reasoning needed in legal answers. This means that evaluation methods need to change completely. To fix the problems with current methods, this study presents a new model-based evaluation metric that is designed to work well with legal QA systems. We are looking into the basic ideas that are needed for this kind of metric, as well as the problems of putting it into practice in the real world, finding the right technological frameworks, creating good evaluation methods. We talk about a theory framework that is based on legal standards and computational linguistics. We also talk about how the metric was created and how it can be used in real life. Our results, which come from thorough tests, show that our suggested measure is better than existing ones. It is more reliable, accurate, and useful for judging legal quality assurance systems. © 2023 IEEE.

关键词： Large Language Model Model-Based Evaluation Metric Natural Language Processing Question Answering Systems Transformer Models

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Developing Accurate Sri Lankan Sign Language to Tamil Vocal And American Sign Language Translation 10

Developing Accurate Sri Lankan Sign Language to Tamil Vocal ...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ratnasingam, Sabaskaran Sakajarasa, Dilaxshan Chamara, Didula Gamage, Aruna Ishara Sri Lanka Institue of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798331532895

This research presents a web-based real-time Sri Lankan Sign Language (SLSL) translation system aimed at bridging communication gaps for individuals with speech and hearing disabilities. Leveraging advanced machine learning, the system translates SLSL gestures into Tamil vocal language and American Sign Language (ASL), supported by video-to-text conversion with Yolo-NAS, voice model and clips of animated 3D characters. Key objectives included accurate vocabulary mapping for selected vocabularies, real-time translation with minimal latency, an audio player to display Tamil voice and animated 3D characters to convey nuanced ASL gestures. The system is integrated as one website, but each translation part has separate pages where users can navigate and fulfill their needs. User feedback highlighted the simple and direct user interface design and fluidity of interactions, particularly the animated 3D characters in SLSL to ASL translation. Challenges included optimizing gesture rendering and expanding vocabulary coverage, which will be addressed in future work. This research underscores the potential of AI-driven solutions for enhancing accessibility and inclusivity in communication with cross-lingual and cross-model communication features. © 2024 IEEE.

关键词： User interfaces

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Forecasting Cryptocurrency Prices Using a Gated Recurrent Unit Neural Network

Forecasting Cryptocurrency Prices Using a Gated Recurrent Un...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Khan, Muhammad Shahzeb Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Ameen, Mohammad Shah, Syed Ali Asghar BUITEMS Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan BUITEMS Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9781665476010

This paper investigates the potential of using a gated recurrent unit (GRU) neural network (NN) for forecasting the prices of three popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). A dataset spanning from October 2021 to October 2022 was collected and used to train and evaluate the performance of the proposed model. The proposed GRU model was evaluated using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE) as evaluation metrics. The results of the study show that the GRU model achieved an RMSE of 366.0601 and a MAPE of 1.7268% for BTC, an RMSE of 37.6678 and a MAPE of 2.3342% for ETH, and an RMSE of 1.0902 and a MAPE of 1.7278% for LTC. The results indicate that the GRU model performed well in forecasting cryptocurrency prices and holds promise as an approach for further research in this field. © 2023 IEEE.

关键词： Bitcoin

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