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De-Nian Yang

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3 papers

1 author row

AAAI Conference 2024 Conference Paper

Social-Aware Group Display Configuration in VR Conference

Bay-Yuan Hsu
Chih-Ya Shen
Hao Shan Yuan
Wang-Chien Lee
De-Nian Yang

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature -- displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality.

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NeurIPS Conference 2022 Conference Paper

ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences

Ting-An Chen
De-Nian Yang
Ming-Syan Chen

Quantization compresses models to low bits for efficient inferences which has received increasing attentions. However, existing approaches focused on balanced datasets, while imbalanced data is pervasive in the real world. Therefore, in this study, we investigate the realistic problem, quantization on class-imbalanced data. We observe from the analytical results that quantizing imbalanced data tends to obtain a large error due to the differences between separate class distributions, which leads to a significant accuracy loss. To address this issue, we propose a novel quantization framework, Class Imbalanced Quantization (ClimbQ) that focuses on diminishing the inter-class heterogeneity for quantization error reduction. ClimbQ first scales the variance of each class distribution and then projects data through the new distributions to the same space for quantization. To guarantee the homogeneity of class variances after the ClimbQ process, we examine the quantized features and derive that the homogeneity satisfies when data size for each class is restricted (bounded). Accordingly, we design a Homogeneous Variance Loss (HomoVar Loss) which reweights the data losses of each class based on the bounded data sizes to satisfy the homogeneity of class variances. Extensive experiments on class-imbalanced and benchmark balanced datasets reveal that ClimbQ outperforms the state-of-the-art quantization techniques, especially on highly imbalanced data.

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AAAI Conference 2018 Conference Paper

On Organizing Online Soirees with Live Multi-Streaming

Chih-Ya Shen
C. P. Kankeu Fotsing
De-Nian Yang
Yi-Shin Chen
Wang-Chien Lee

The popularity of live streaming has led to the explosive growth in new video contents and social communities on emerging platforms such as Facebook Live and Twitch. Viewers on these platforms are able to follow multiple streams of live events simultaneously, while engaging discussions with friends. However, existing approaches for selecting live streaming channels still focus on satisfying individual preferences of users, without considering the need to accommodate real-time social interactions among viewers and to diversify the content of streams. In this paper, therefore, we formulate a new Social-aware Diverse and Preferred Live Streaming Channel Query (SDSQ) that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2approximation algorithm with a guaranteed error bound. We perform a user study on Twitch with 432 participants to validate the need of SDSQ and the usefulness of SDSSel. We also conduct large-scale experiments on real datasets to demonstrate the superiority of the proposed algorithm over several baselines in terms of solution quality and efﬁciency.

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