http://repository.sungshin.ac.kr/handle/2025.oak/180 2026-05-19T17:39:37Z http://repository.sungshin.ac.kr/handle/2025.oak/8875 Title: MDC+: A Cooperative Approach to Memory-Efficient Fork-Based Checkpointing for In-Memory Database Systems Author(s): 박지웅; 민철기; 염헌영; 정형수 Abstract: Consistent checkpointing is a critical for in-memory databases (IMDBs) but its resource-intensive nature poses challenges for small- and medium-sized deployments in cloud environments, where memory utilization directly affects operational costs. Although traditional fork-based checkpointing offers merits in terms of performance and implementation simplicity, it incurs a considerable rise in memory footprint during checkpointing, particularly under update-intensive workloads. Memory provisioning emerges as a practical remedy to handle peak demands without compromising performance, albeit with potential concerns related to memory over-provisioning. In this article, we propose MDC+, a memory-efficient fork-based checkpointing scheme designed to maintain a reasonable memory footprint during checkpointing by leveraging collaboration among an IMDB, a user-level memory allocator, and the operating system. We explore two key techniques within the checkpointing scheme: (1) memory dump-based checkpointing, which enables early memory release, and (2) hint-based segregated memory allocation, which isolates immutable and updatable data to minimize page duplication. Our evaluation demonstrates that MDC+ significantly lowers peak memory footprint during checkpointing without affecting throughput or checkpointing time. 2025-06-29T15:00:00Z http://repository.sungshin.ac.kr/handle/2025.oak/8758 Title: Speed-Aware Audio-Driven Speech Animation using Adaptive Windows Author(s): 정선진; Yeongho Seol; Kwanggyoon Seo; Hyeonho Na; Seonghyeon Kim; Vanessa Tan; Junyong Noh Abstract: We present a novel method that can generate realistic speech animations of a 3D face from audio using multiple adaptive windows. In contrast to previous studies that use a fixed size audio window, our method accepts an adaptive audio window as input, reflecting the audio speaking rate to use consistent phonemic information. Our system consists of three parts. First, the speaking rate is estimated from the input audio using a neural network trained in a self-supervised manner. Second, the appropriate window size that encloses the audio features is predicted adaptively based on the estimated speaking rate. Another key element lies in the use of multiple audio windows of different sizes as input to the animation generator: a small window to concentrate on detailed information and a large window to consider broad phonemic information near the center frame. Finally, the speech animation is generated from the multiple adaptive audio windows. Our method can generate realistic speech animations from in-the-wild audios at any speaking rate, i.e., fast raps, slow songs, as well as normal speech. We demonstrate via extensive quantitative and qualitative evaluations including a user study that our method outperforms state-ofthe-art approaches. 2025-01-31T15:00:00Z http://repository.sungshin.ac.kr/handle/2025.oak/8671 Title: Deep-Learning-Based Facial Retargeting Using Local Patches Author(s): 정선진 Abstract: In the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch-based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re-enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to styl 2025-01-31T15:00:00Z http://repository.sungshin.ac.kr/handle/2025.oak/8637 Title: Dalio: In-Kernel Centralized Replication for Key-Value Stores Author(s): 김규영 Abstract: Replication is commonly used in distributed key-value stores for high availability. Recent works show that centralized replication provides high throughput through low-overhead write coordination and consistency-aware read forwarding. Unfortunately, they rely on specialized hardware, which is deploy-challenging and poses various limitations. To this end, we present Dalio, a software-based centralized replication system that does not require extra hardware while supporting high throughput. Our key idea is to offload the replication function to per-shard load balancers with eBPF, an emerging kernel-native technique. By building a replication coordinator with eBPF, we can avoid burdensome kernel networking stack overhead. Our experimental results show that Dalio achieves throughput better than the vanilla Linux by up to 2.05x and is comparable to a hardware-based solution. 2025-01-31T15:00:00Z