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/exper...
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ISBN:
(数字)9798331514846
ISBN:
(纸本)9798331525637
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.
Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigati...
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ISBN:
(数字)9798331514846
ISBN:
(纸本)9798331525637
Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigation routes in an immersive virtual environment (IVE) on navigation performance and user experience in a corresponding real-world indoor setting. We developed a guide system with two distinct audiovisual representations: a human agent guide and a symbol-based guide. A preliminary user study (N = 10) was conducted to evaluate the system. While no significant differences were observed between the two guide conditions, the findings reveal valuable insights into user-perceived confidence and enjoyment during real-world navigation tasks. Contrary to our expectations, the symbol-based guide elicited slightly higher positive scores compared to the human agent guide. We discuss these findings and outline directions for future research.
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 me...
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ISBN:
(数字)9798331514846
ISBN:
(纸本)9798331525637
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.
Many countries are severely affected by COVID-19, and massive efforts are required to mitigate the terrible impacts of the COVID-19 global epidemic. To stop COVID-19 from spreading widely and minimize COVID-19 patient...
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This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to i...
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This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. In addition, it is found that with the increase of context complexity, the traditional ontology inference model has limitations in abnormal behavior recognition, especially in the case of high data redundancy, which tends to lead to a decrease in recognition accuracy. This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.
Thermal runaway in lithium-ion batteries (LIBs) is a critical risk, potentially leading to fires and explosions. This phenomenon can be triggered by overcharging, short-circuiting, physical damage, manufacturing defec...
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ISBN:
(数字)9798331516116
ISBN:
(纸本)9798331516123
Thermal runaway in lithium-ion batteries (LIBs) is a critical risk, potentially leading to fires and explosions. This phenomenon can be triggered by overcharging, short-circuiting, physical damage, manufacturing defects, and overheating. Effective mitigation of thermal runaway relies on precise and close monitoring of individual cells within the battery pack, a task often complicated by the complexity and cost of implementing temperature sensors on each cell in automotive applications. This study demonstrates the importance of individual cell temperature monitoring by intentionally including an unhealthy cell (C#12) in a 14-cell LIB 21700 module equipped with an automotive-grade battery management system (BMS) from NXP®. Experimental results reveal that despite efficient BMS balancing, the weak cell exhibited a significantly higher temperature rise, with a final temperature difference of 6°C and a voltage difference of over 200 mV compared to healthy cells. These findings underscore the potential for thermal failure and runaway if individual cell temperatures are not closely monitored. Additionally, reliance on voltage-based control alone can lead to suboptimal battery pack utilization, as evidenced by a 5% capacity loss due to the weak cell. This research highlights the necessity of monitoring the temperature of each cell to prevent thermal runaway and ensure efficient battery performance.
Mobile games are a popular, cheap, and convenient source of entertainment. However, the increasing complexity of these games is making it more difficult to thoroughly plan, monitor, and control mobile game development...
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ISBN:
(数字)9798331533038
ISBN:
(纸本)9798331533045
Mobile games are a popular, cheap, and convenient source of entertainment. However, the increasing complexity of these games is making it more difficult to thoroughly plan, monitor, and control mobile game development projects. Possessing the ability to accurately estimate the development effort for these projects early in their life cycle is expected to make the jobs of their managers much easier. The objective of this study was to build a calibrated and validated early effort prediction model for mobile games developed using the Unity game engine. First, we identified factors that potentially influence development effort in these projects by conducting an industrial survey. Once these factors were identified, we gathered information about these factors and actual development effort for more than 100 real mobile games developed by different game studios. Then, we performed a simple linear regression (SLR) analysis to rank these factors with respect to their individual influence on mobile game development effort. Finally, we used the games data collected earlier to build, assess, and validate a forward step-wise multiple linear regression (FSMLR)-based model for early effort prediction of such mobile games. The MMRE value of this model is 0.136 and its PRED (25) value is $85.98 \%$. These values indicate that this model has reasonably good estimation accuracy. It can be a useful tool in the arsenal of managers interested in effectively planning their mobile game development projects early in the life cycle.
GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for in...
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The Kolmogorov–Arnold Network (KAN) is a new-generation neural network. It provides an alternative to multilayer perceptrons (MLPs). PoolFormer showed that pooling alone can mix features efficiently. We propose PoolK...
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The Kolmogorov–Arnold Network (KAN) is a new-generation neural network. It provides an alternative to multilayer perceptrons (MLPs). PoolFormer showed that pooling alone can mix features efficiently. We propose PoolKANNeXt, a CNN that merges the KAN structure with pooling-based feature mixing. This design targets high accuracy with a low number of the learnable parameters. We evaluated PoolKANNeXt on six image datasets, five biomedical and one general (CIFAR-10). The model comprises four stages. In the stem stage, a ConvNeXt-style patchify block converts each 224 × 224 × 3 image into a 56 × 56 × 96 tensor. In the main stage, average pooling first mixes local features; then two parallel 3 × 3 convolutions—one followed by GELU and the other by Swish—extract complementary representations, and a 1 × 1 convolution scales the combined output and adds it back to the input via a residual connection. In the downsampling stage, strided 3 × 3 convolutions halve spatial dimensions and double the channel count. In the output stage, global average pooling produces a feature vector that feeds into a softmax classifier. PoolKANNeXt achieved over 90 % accuracy on all datasets and reached 99.43 % on CIFAR-10, ranking among the top five models on that benchmark. PoolKANNeXt offers a lightweight yet powerful architecture. Its innovative combination of pooling and dual-activation KAN blocks yields strong performance across diverse tasks. The design is scalable and adaptable to larger or more complex datasets.
Survivors of domestic abuse face significant challenges securing recovery resources, such as housing, mental health care, and social connections. Accessing these resources requires survivors to disclose their status a...
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