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Three-state log-aware buffer management scheme for flash-based consumer electronics
Rize Jin,Hyung-Ju Cho,Tae-Sun Chung IEEE 2013 IEEE transactions on consumer electronics Vol.59 No.4
<P>Major digital consumer electronics such as smartphones and tablet PCs are equipped with flash memory because of its many advantages. However, its distinguishing characteristics, including erase-before-update, asymmetric read/write/erase cost and limited number of erase cycles, make it necessary to reconsider existing storage access designs in order to explore the hardware potential. For example, the buffer replacement scheme for flash-based systems should not only consider the cache hit ratio, but also the relatively heavy write and erase costs that are caused by flushing dirty pages. Most of the recent studies on buffer design focus on a clean-first LRU (Least Recently Used) strategy that evicts clean pages prior to dirty pages, in order to minimize the write access to flash. However, all of them failed to distinguish the cached pages that may have different effects on the flash device under various storage mangers. This paper proposes a three-state log-aware buffer management scheme, called TSLA, which considers not only the imbalance of read/write costs of flash memory but also the log block thrashing, associativity, and space utilization problems of log-based FTLs (flash translation layers). Experimental results show that the proposed solution is effective for reducing the garbage collection overhead under various FTLs, such as BAST, FAST and IPL.</P>
A Safe-Region Approach to a Moving <i>k</i> -RNN Queries in a Directed Road Network
Zeberga, Kamil,Jin, Rize,Cho, Hyung-Ju,Chung, Tae-Sun World Scientific Publishing Company 2017 Journal of circuits, systems, and computers Vol.26 No.5
<P>In road networks, <TEX>$ k$</TEX>-range nearest neighbor (<TEX>$ k$</TEX>-RNN) queries locate the <TEX>$ k$</TEX>-closest neighbors for every point on the road segments, within a given query region defined by the user, based on the network distance. This is an important task because the user's location information may be inaccurate; furthermore, users may be unwilling to reveal their exact location for privacy reasons. Therefore, under this type of specific situation, the server returns candidate objects for every point on the road segments and the client evaluates and chooses exact <TEX>$ k$</TEX> nearest objects from the candidate objects. Evaluating the query results at each timestamp to keep the freshness of the query answer, while the query object is moving, will create significant computation burden for the client. We therefore propose an efficient approach called a safe-region-based approach (SRA) for computing a safe segment region and the safe exit points of a moving nearest neighbor (NN) query in a road network. SRA avoids evaluation of candidate answers returned by the location-based server since it will have high computation cost in the query side. Additionally, we applied SRA for a directed road network, where each road network has a particular orientation and the network distances are not symmetric. Our experimental results demonstrate that SRA significantly outperforms a conventional solution in terms of both computational and communication costs.</P>
A Novel Cross Channel Self-Attention based Approach for Facial Attribute Editing
( Meng Xu ),( Rize Jin ),( Liangfu Lu ),( Tae-sun Chung ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15~28% and 25~100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model.
An Distributed Method for Calculating Entropy of Large-scale Sequential Data
Zhen Zhang,Hongqiang Li,Rize Jin 한국정보통신학회 2021 2016 INTERNATIONAL CONFERENCE Vol.12 No.1
Various IoT devices constantly generate data in terms of time, which may form a large-scale of sequential data. Entropy calculation of a big data input will lead to unacceptable time, even not able to obtain the output. In this paper, we propose a distributed method for accelerating the entropy calculation process. Utilize a high performance host sever to deploy a distributed computing platform by server virtualization technique. Run independent R environment for the entropy calculation in multiple computing nodes and adopt Java multi-thread technique in one control node. From the experiment results and the analysis, we can conclude that the proposed distributed scheme is efficient and feasible for dealing with large-scale sequential data.