检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Vehicle Re-Identification in Aerial Imagery: dataset and Approach

Vehicle Re-Identification in Aerial Imagery: Dataset and App...

引用

International Conference on computer Vision (ICCV)

作者： Peng Wang Bingliang Jiao Lu Yang Yifei Yang Shizhou Zhang Wei Wei Yanning Zhang School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs at different locations, with diverse view-angles and flight-altitudes. We manually label a variety of vehicle attributes, including vehicle type, color, skylight, bumper, spare tire and luggage rack. Furthermore, for each vehicle image, the annotator is also required to mark the discriminative parts that helps them to distinguish this particular vehicle from others. Besides the dataset, we also design a specific vehicle ReID algorithm to make full use of the rich annotation information. It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforming the evaluated baselines and state-of-the-art vehicle ReID approaches.

关键词： Image color analysis Cameras Feature extraction Task analysis Image resolution Tires Visualization

来源：评论

学校读者我要写书评

暂无评论

Viral drug prediction with matrix decomposition and two-layer heterogeneous graph inference

Research Square

引用

Research Square 2021年

作者： Qu, Jia Cheng, Xiao-Long Bian, Ze-Kang Ni, Tong-Guang Guan, Na-Na School of Computer Science and Artificial Intelligence & Aliyun School of Big Data Changzhou University Changzhou213164 China School of AI & Computer Science Jiangnan University Wuxi214122 China College of Big Data Statistics Guizhou University of Finance and Economics Guiyang550025 China School of Information and Control Engineering China University of Mining and Technology China

Recently, the association prediction between viruses and drugs has drawn more and more attention. A growing number of studies have shown that the problem of antiviral drug resistance is increasing and has become a major problem plaguing the medical community. Moreover, the development cycle of new drugs is long and requires a lot of funds. If new viruses emerge, effective antiviral drugs are urgently needed. Therefore, effective calculation methods are required to predict potential antiviral drugs. In this paper, we developed a computational model of Matrix Decomposition and Heterogeneous Graph based Inference for Drug-Virus Association (MDHGIVDA) to predict potential drug-virus associations. MDHGIVDA integrated virus sequence similarity, drug chemical structure similarity, drug side effect similarity, Gaussian interaction profile kernel similarity for drugs and viruses, new drug-virus associations matrix obtained by matrix decomposition to discover new drug-virus associations. Due to the use of matrix factorization and heterogeneous graphs, our model has a high prediction accuracy compared with the previous four models. In the global and local leave-one-out cross validation (LOOCV), MDHGIVDA obtained area under the receiver operating characteristics curve (AUC) of 0.8528 and AUC of 0.8532, respectively. In addition, in the five-fold cross validation, the AUC and the standard deviation is 0.8299 0.0037, which shows that MDHGIVDA has stability and high prediction accuracy. In the case studies of three important viruses, 18, 14, and 16 out of the top 20 predicted drugs for Zika virus (ZIKV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), Human Immunodeficiency Virus-1 (HIV-1) were verified respectively by searching the literature on PubMed. These results showed that MDHGIVDA is effective in predicting potential drug-virus associations. © 2021, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Edge-Based Video Analytics: A Survey

arXiv

引用

arXiv 2023年

作者： Hu, Miao Luo, Zhenxiao Pasdar, Amirmohammad Lee, Young Choon Zhou, Yipeng Wu, Di School of Computer Science and Engineering The Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China School of Computing Macquarie University Sydney Australia

Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge computing in essence aims to overcome this hindrance by processing most video data making use of edge servers, such as small-scale on-premises server clusters, server-grade computing resources at mobile base stations and even mobile devices like smartphones and tablets;hence, the term edge-based video analytics. However, the actual realization of such analytics requires more than the simple, collective use of edge servers. In this paper, we survey state-of-the-art works on edge-based video analytics with respect to applications, architectures, techniques, resource management, security and privacy. We provide a comprehensive and detailed review on what works, what doesn't work and why. These findings give insights and suggestions for next generation edge-based video analytics. We also identify open issues and research directions. © 2023, CC BY.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Partial Label Feature Selection: An Adaptive Approach

引用

IEEE Transactions on Knowledge and data engineering 2024年第8期36卷 4178-4191页

作者： Zan Zhang Jialu Yao Lin Liu Jiuyong Li Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei Anhui China UniSA STEM University of South Australia Adelaide SA Australia Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei Anhui China

As an emerging weakly supervised learning framework, partial label learning aims to induce a multi-class classifier from ambiguous supervision information where each training example is associated with a set of candidate labels, among which only one is the true label. Traditional feature selection methods, either for single label and multiple label problems, are not applicable to partial label learning as the ambiguous information contained in the label space obfuscates the importance of features and misleads the selection process. This makes the selection of a proper feature subset from partial label examples particularly challenging, and therefore has rarely been investigated. In this paper, we propose a novel feature selection algorithm for partial label learning, named PLFS, which considers not only the relationships between features and labels, but also exploits the relationships between instances to select the most informative and important features to enhance the performance of partial label learning. PLFS constructs an adaptive weighted graph to exploit the similarity information among instances, differentiate the label space and weight the feature space, which leads to the selection of a proper feature subset. Extensive experiments over a broad range of benchmark data sets clearly validate the effectiveness of our proposed feature selection approach.

关键词： Feature extraction Supervised learning Training Knowledge engineering Labeling Classification algorithms Big data

来源：评论

学校读者我要写书评

暂无评论

Finger Vein Biometric Identification Using Discretization Method 2

Finger Vein Biometric Identification Using Discretization Me...

引用

2nd International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2020, ICE4CT 2020

作者： Yahaya, Y.H. Leng, W.Y. Shamsuddin, S.M. Computer Science Department Faculty of Science and Defence Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur53100 Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai JB83100 Malaysia UTM Big Data Centre Universiti Teknologi Malaysia Skudai JB83100 Malaysia

Over the past years, finger vein identification has gaining increasing attention in biometrics. It has many advantages as compared to other biometrics such as living-body identification, difficult to counterfeit because it resides underneath the finger skin and noninvasiveness. Finger vein feature extraction plays an important role in finger vein identification. The performance of finger vein identification is highly depending on the meaningful extracted features from feature extraction process. However, most of the works focus on how to extract the individual features and not presenting the individual characteristic of finger vein patterns with systematic representation. This paper proposed an improved scheme of finger vein feature extraction method by adopting discretization method. The extracted features will be represented systematically way in order to make classification task easier and increase the identification accuracy rate. The experimental result shows that the accuracy rate of identification of the proposed framework using Discretization is above 98.0%. © Published under licence by IOP Publishing Ltd.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Segmentation for Factoring Customers: Using Unsupervised Machine Learning Algorithms

Segmentation for Factoring Customers: Using Unsupervised Mac...

引用

2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Ayyildiz, Nur Seher Akcay, Ahmet Yalcuva, Berat Sayar, Alperen Ertugrul, Seyit Cakar, Tuna Mef University Graduate School of Science & Eng. Istanbul Turkey Data Scientist Tam Finans R&d Department Istanbul Turkey Head of R&d Tam Finans R&d Department Istanbul Turkey Mef University Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350306590

Nowadays the fact that technology facilitates data collection is an important opportunity, as well as making the management of all this data difficult and makes no sense unless it is well processed. This stored data is extremely important, and companies use data provided by their customers. Catching the needs of the customer profiles of the changing world is now a necessity and takes the first place for companies. With the increase in the amount of stored data over time, it has become difficult to establish a relationship between the data and to separate them from each other. At this point, machine learning methods have become more involved in our lives. In this study, what segmentation is and its change over the years are mentioned. It has been mentioned which machine learning techniques will be useful in data selection. Then, possible machine learning methods are shown in real life segmentation problem by using the domestic factoring company's customer check data. Since this study aims to group unlabeled data, unsupervised learning techniques are emphasized. Among these methods, Hierarchical Clustering, DBSCAN, Gaussian Mixture Modeling methods, Fuzzy c- Means were used as well as the most popular K-Means algorithm. When the clustering results were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering could not produce expected results. It was observed that the best results were obtained with the K-Means and Fuzzy c - Means algorithms. © 2023 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

FineFormer: Fine-Grained Adaptive Object Transformer for Image Captioning

FineFormer: Fine-Grained Adaptive Object Transformer for Ima...

引用

IEEE International Conference on data Mining (ICDM)

作者： Bo Wang Zhao Zhang Jicong Fan Mingbo Zhao Choujun Zhan Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Information Science and Technology Donghua University Shanghai China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China School of Information Engineering Zhengzhou University Zhengzhou China

Image captioning is still a challenging task aiming at describing the contents of image by words. Current image caption methods usually assume the object relation to be important if the semantic and spatial geometric relationships between objects are close and large, but the relations meeting this assumption are not necessarily important to describe the contents of image in a fine-grained way. That is, the importance of fine-grained object relations is not properly taken into account. Besides, current Transformer based image caption models also fail to consider the importance of fine-grained objects, since they generate all the words of a sentence at one time, which cannot Figure out which objects are more important and vice versa. In this paper, we propose a novel Fine-grained Adaptive Object Transformer (FineFormer) network, which can jointly discover the importance of fine-grained objects and object relations for image captioning. Specifically, a new concept of adaptive soft-foreground attention is proposed to highlight the fine-grained objects dominating the descriptive contents. To characterize and calculate the important relations between fine-grained objects, we also propose an adaptive object relation attention to refine the object relation from the generation process of relation. As such, FineFormer can describe the contents of image more accurately, by reducing the interference of unimportant objects in the background. Extensive experiments on the highly-competitive MS-COCO dataset demonstrated the superiority of our FineFormer.

关键词： Adaptation models Adaptive systems Current transformers Semantics Interference data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

data Processing Technology for Network Abnormal Traffic Detection 4

Data Processing Technology for Network Abnormal Traffic Dete...

引用

4th International Conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Kun Fu, Yu Duan, Xueyuan Xu, Jianqiao Liu, Taotao Department of Information Security Naval University of Engineering Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization(PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Constructing a Comprehensive Clinical database Integrating Patients' data from Intensive Care Units and General Wards 12

Constructing a Comprehensive Clinical Database Integrating P...

引用

12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Liu, Tongbo Liu, Xiaoli Fan, Yong Xu, Haoran Ng, Yeuk Lam Li, Peiyao Xue, Wanguo Zhang, Zhengbo Chinese PLA General Hospital Department of Computer Management and Application Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Chinese PLA General Hospital Department of Biomedical Engineering Beijing China University of Toronto Faculty of Arts Science Toronto Canada Medical Big Data Center Chinese PLA General Hospital Beijing China

ISBN: (纸本)9781728148526

Collection and analysis of large volumes of ICU data are invaluable to the advancement of clinical knowledge, and large-scale ICU databases have been effective resources to understand risk factors and perform predictive analysis by using machine learning. This paper introduces the construction of a comprehensive clinical database: PLAGH-ICU database, which integrated data from nine intensive care units, emergency department and general wards. data from several sources were extracted and integrated in the database, including patient demographics, hospital administrative data, physiological data, medications, lab test, fluid balance data, notes and reports etc. Detailed information about the database, such as patient characteristics, disease distribution, category of data, data records were illustrated. As far as we know, this is the first comprehensive ICU database developed and used as a research database in datathon event in China. Such kind of database will promote the research progress in critical care medicine, as well as the development of modern hospital information system in China. © 2019 IEEE.

关键词： Intensive care units

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：