Journal of Computer Science and Technology最新文献

{"title":"Enhancing Storage Efficiency and Performance: A Survey of Data Partitioning Techniques","authors":"Peng-Ju Liu, Cui-Ping Li, Hong Chen","doi":"10.1007/s11390-024-3538-1","DOIUrl":"https://doi.org/10.1007/s11390-024-3538-1","url":null,"abstract":"<p>Data partitioning techniques are pivotal for optimal data placement across storage devices, thereby enhancing resource utilization and overall system throughput. However, the design of effective partition schemes faces multiple challenges, including considerations of the cluster environment, storage device characteristics, optimization objectives, and the balance between partition quality and computational efficiency. Furthermore, dynamic environments necessitate robust partition detection mechanisms. This paper presents a comprehensive survey structured around partition deployment environments, outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are addressed. We discuss partitioning features pertaining to database schema, table data, workload, and runtime metrics. We then delve into the partition generation process, segmenting it into initialization and optimization stages. A comparative analysis of partition generation and update algorithms is provided, emphasizing their suitability for different scenarios and optimization objectives. Additionally, we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and solutions. This survey aims to foster the implementation, deployment, and updating of high-quality partitions for specific system scenarios.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAT: A Simple yet Effective Cross-Attention Transformer for One-Shot Object Detection","authors":"Wei-Dong Lin, Yu-Yan Deng, Yang Gao, Ning Wang, Ling-Qiao Liu, Lei Zhang, Peng Wang","doi":"10.1007/s11390-024-1743-6","DOIUrl":"https://doi.org/10.1007/s11390-024-1743-6","url":null,"abstract":"<p>Given a query patch from a novel class, one-shot object detection aims to detect all instances of this class in a target image through the semantic similarity comparison. However, due to the extremely limited guidance in the novel class as well as the unseen appearance difference between the query and target instances, it is difficult to appropriately exploit their semantic similarity and generalize well. To mitigate this problem, we present a universal Cross-Attention Transformer (CAT) module for accurate and efficient semantic similarity comparison in one-shot object detection. The proposed CAT utilizes the transformer mechanism to comprehensively capture bi-directional correspondence between any paired pixels from the query and the target image, which empowers us to sufficiently exploit their semantic characteristics for accurate similarity comparison. In addition, the proposed CAT enables feature dimensionality compression for inference speedup without performance loss. Extensive experiments on three object detection datasets MS-COCO, PASCAL VOC and FSOD under the one-shot setting demonstrate the effectiveness and efficiency of our model, e.g., it surpasses CoAE, a major baseline in this task, by 1.0% in average precision (AP) on MS-COCO and runs nearly 2.5 times faster.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random Subspace Sampling for Classification with Missing Data","authors":"Yun-Hao Cao, Jian-Xin Wu","doi":"10.1007/s11390-023-1611-9","DOIUrl":"https://doi.org/10.1007/s11390-023-1611-9","url":null,"abstract":"<p>Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large errors. In this paper, we propose a random subspace sampling method, RSS, by sampling missing items from the corresponding feature histogram distributions in random subspaces, which is effective and efficient at different levels of missing data. Unlike most established approaches, RSS does not train on fixed imputed datasets. Instead, we design a dynamic training strategy where the filled values change dynamically by resampling during training. Moreover, thanks to the sampling strategy, we design an ensemble testing strategy where we combine the results of multiple runs of a single model, which is more efficient and resource-saving than previous ensemble methods. Finally, we combine these two strategies with the random subspace method, which makes our estimations more robust and accurate. The effectiveness of the proposed RSS method is well validated by experimental studies.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective","authors":"Xiao-Fei Liao, Wen-Ju Zhao, Hai Jin, Peng-Cheng Yao, Yu Huang, Qing-Gang Wang, Jin Zhao, Long Zheng, Yu Zhang, Zhi-Yuan Shao","doi":"10.1007/s11390-024-4150-0","DOIUrl":"https://doi.org/10.1007/s11390-024-4150-0","url":null,"abstract":"<p>Graph processing has been widely used in many scenarios, from scientific computing to artificial intelligence. Graph processing exhibits irregular computational parallelism and random memory accesses, unlike traditional workloads. Therefore, running graph processing workloads on conventional architectures (e.g., CPUs and GPUs) often shows a significantly low compute-memory ratio with few performance benefits, which can be, in many cases, even slower than a specialized single-thread graph algorithm. While domain-specific hardware designs are essential for graph processing, it is still challenging to transform the hardware capability to performance boost without coupled software codesigns. This article presents a graph processing ecosystem from hardware to software. We start by introducing a series of hardware accelerators as the foundation of this ecosystem. Subsequently, the codesigned parallel graph systems and their distributed techniques are presented to support graph applications. Finally, we introduce our efforts on novel graph applications and hardware architectures. Extensive results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Qubit Mapping Based on Tabu Search","authors":"Hui Jiang, Yu-Xin Deng, Ming Xu","doi":"10.1007/s11390-023-2121-5","DOIUrl":"https://doi.org/10.1007/s11390-023-2121-5","url":null,"abstract":"<p>The goal of qubit mapping is to map a logical circuit to a physical device by introducing additional gates as few as possible in an acceptable amount of time. We present an effective approach called Tabu Search Based Adjustment (TSA) algorithm to construct the mappings. It consists of two key steps: one is making use of a combined subgraph isomorphism and completion to initialize some candidate mappings, and the other is dynamically modifying the mappings by TSA. Our experiments show that, compared with state-of-the-art methods, TSA can generate mappings with a smaller number of additional gates and have better scalability for large-scale circuits.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement","authors":"Zi-Nuo Li, Xu-Hang Chen, Shu-Na Guo, Shu-Qiang Wang, Chi-Man Pun","doi":"10.1007/s11390-024-3414-z","DOIUrl":"https://doi.org/10.1007/s11390-024-3414-z","url":null,"abstract":"<p>Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval","authors":"Younhyun Jung, Jim Kong, Bin Sheng, Jinman Kim","doi":"10.1007/s11390-024-3419-7","DOIUrl":"https://doi.org/10.1007/s11390-024-3419-7","url":null,"abstract":"<p>Direct volume rendering (DVR) is a technique that emphasizes structures of interest (SOIs) within a volume visually, while simultaneously depicting adjacent regional information, e.g., the spatial location of a structure concerning its neighbors. In DVR, transfer function (TF) plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of them. TF generation typically involves non-intuitive trial-and-error optimization of rendering parameters, which is time-consuming and inefficient. Attempts at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain experts. In these approaches, a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the SOIs. Although these approaches potentially reduce the workload to generate the TFs, they, however, require manual TF navigation of the knowledge database, as well as the likely fine tuning of the selected TF to suit the input. In this work, we propose a TF design approach, CBR-TF, where we introduce a new content-based retrieval (CBR) method to automatically navigate the knowledge database. Instead of pre-designed TFs, our knowledge database contains volumes with SOI labels. Given an input volume, our CBR-TF approach retrieves relevant volumes (with SOI labels) from the knowledge database; the retrieved labels are then used to generate and optimize TFs of the input. This approach largely reduces manual TF navigation and fine tuning. For our CBR-TF approach, we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the profile. For the regional spatial semantics, we adopt a convolutional neural network to obtain high-level image feature representations. For the intensity profile, we extend the dynamic time warping technique to address subtle alignment differences between similar profiles (SOIs). Finally, we propose a two-stage CBR scheme to enable the use of these two different feature representations in a complementary manner, thereby improving SOI retrieval performance. We demonstrate the capabilities of our CBR-TF approach with comparison with a conventional approach in visualization, where an intensity profile matching algorithm is used, and also with potential use-cases in medical volume visualization.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods and tools for the development and evaluation of refactorings to improve the user experience in web applications.","authors":"Juan Cruz Gardey","doi":"10.24215/16666038.24.e06","DOIUrl":"https://doi.org/10.24215/16666038.24.e06","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Governance of Technologies and Information Systems for the Higher Education: Systematic Mapping of Study","authors":"Vicente Merchán-Rodríguez, C. Juiz","doi":"10.24215/16666038.24.e05","DOIUrl":"https://doi.org/10.24215/16666038.24.e05","url":null,"abstract":"The pandemic has led to more attention to how technologies and information systems (T&IS) are governed in higher education institutions (HEI). However, many of the aspects are used to study them do not gather the expectations of those who benefit from these studies and the institutions are shocked as a result that major aspects of governance are not considered. This paper seeks to summarize the current knowledge that is available regarding the strategies adopted to know the state of governance of T&IS for the higher education between 2015 and 2021 years. A systematic mapping of studies was carried out to review the aspects that have been used by the researchers in the different studies. The results show that 30% of the works reviewed are broad and cover variables of operation, management, and government; they provide a renewed set of judgments, dimensions, and indicators to measure the level of institutional achievement, in addition COBIT v5 is the most widely used reference framework to measure capacities in governance of technology and information system for processes. The results obtained have made it possible to identify several opportunities in research such as creating a useful framework for HEI.","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GLC-Frame: A Framework and Library for Exploration of Multidimensional Data with General Line Coordinates","authors":"Leandro Luque, A. Antonini, M. L. Ganuza, Silvia Castro","doi":"10.24215/16666038.24.e02","DOIUrl":"https://doi.org/10.24215/16666038.24.e02","url":null,"abstract":"General Line Coordinates (GLC) are a relatively new set of line-based representations for visualizing multidimensional data with the distinctive characteristics of being reversible and lossless. Given these characteristics, the GLC have a high potential for exploratory multidimensional data analysis, however only partial implementations of some of the GLC techniques are available for the visualization community. In this paper, we present the GLC-Frame, an online exploration tool that supports a dual view and allows users to upload their own dataset and interactively explore the different GLC representations without writing code. We also present the GLC-Vis Library, an open-source data visualization library supporting GLC along with traditional interactions. Finally, we provide a set of usage examples showing how the different techniques behave in both the occlusion and the cluster identification problem. In addition, we present the interactions on GLC representations using the cars dataset. Both the GLC-Frame and the GLC-Vis Library provide an exploration space that will allow the visualization community to use these new techniques and evaluate their potential.","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}