检索结果-内蒙古大学图书馆

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Pengcheng Yao Long Zheng Zhen Zeng Yu Huang Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering UNSW Sydney Australia

ISBN: (数字)9781728173832

ISBN: (纸本)9781728173849

Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip memory to avoid off-chip communications. However, graph mining exhibits substantial random accesses from not only vertex dimension but also edge dimension (with the latter being excessively more complex than the former), leading to significant degradations in terms of both performance and energy *** observe that the most random memory requests arising in graph mining come from accessing a small fraction of valuable (vertex and edge) data when handling real-world graphs. To exploit this extension locality with maximum parallelism, we architect GRAMER, the first graph mining accelerator. GRAMER contains a specialized memory hierarchy, where the valuable data (precisely identified through a cost-efficient heuristic) is permanently resident in a high-priority memory while others are maintained in a cache-like memory under a lightweight replacement policy. The specific pipelined processing units are carefully designed to maximize computational parallelism. GRAMER is also equipped with a work-stealing mechanism to reduce load imbalance. We have implemented GRAMER on a Xilinx Alveo U250 accelerator card. Compared with two state-of-the-art CPU-based graph mining systems, Fractal and RStream, running on a 14-core Intel E5-2680 v4 processor, GRAMER achieves not only considerable speedups (1.11 × ~ 129.95 ) but also significant energy savings (5.79 × ~ 678.34×)

关键词： Memory management Parallel processing Feature extraction Energy efficiency Fractals System-on-chip data mining

来源：评论

学校读者我要写书评

暂无评论

MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

引用

Computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

来源：评论

学校读者我要写书评

暂无评论

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

引用

Nature Communications 2023年第1期14卷 6870页

作者： Cai, Mingxuan Wang, Zhiwei Xiao, Jiashun Hu, Xianghong Chen, Gang Yang, Can Department of Biostatistics City University of Hong Kong Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou 511458 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data Shenzhen 518172 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China WeGene Shenzhen Zaozhidao Technology Co. Ltd Shenzhen 518040 China Graduate Affairs Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution. © 2023, The Author(s).

关键词：

来源：评论

学校读者我要写书评

暂无评论

T-EGAT: A Temporal Edge Enhanced Graph Attention Network for Tax Evasion Detection

T-EGAT: A Temporal Edge Enhanced Graph Attention Network for...

引用

IEEE International Conference on big data

作者： Yiyang Wang Qinghua Zheng Jianfei Ruan Yuda Gao Yan Chen Xuanya Li Bo Dong SPKLSTN Lab School of Computer Science and Technology Xi’an Jiaotong University Baidu Inc. National Engineering Lab of Big Data Analytics School of Distance Education Xi’an Jiaotong University

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Tax evasion refers to the illegal act of taxpayers using deception and concealment to avoid paying taxes. How to detect tax evasion effectively is always an important topic for the government and academic researchers. Recent research has proposed using machine learning technologies to detect tax evasion and has achieved good results in some specific conditions. However, recent methods have three shortcomings. First, recent methods mainly use the basic features extracted based on expert experience. Second, recent methods do not make full use of the edge features of the transaction network. Third, recent methods cannot adapt to a dynamic transaction network. To overcome these challenges, we propose a novel tax evasion detection method, the temporal edge enhanced graph attention network (T-EGAT), which combines the edge enhanced graph attention network (EGAT) and the recurrent weighted average unit (RWA). Specifically, the EGAT is used to learn complex topological structures for capturing spatial dependence and the RWA is used to learn the dynamic changes of transaction data for capturing temporal dependence. Experimental tests using real-world tax data demonstrate that our method achieves better performance at detecting tax evaders than existing methods.

关键词： Image edge detection Neural networks Finance big data Feature extraction data models Task analysis

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Learning to Rank with Uncertain data 16th

Semi-supervised Learning to Rank with Uncertain Data

引用

16th Web Information Systems and Applications Conference, WISA 2019

作者： Zhang, Xin Zhao, ZhongQi Liu, ChengHou Zhang, Chen Cheng, Zhi Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Technology Department of Computer Science and Technology Hefei University Hefei China Suzhou Vocational University Suzhou China Changan Automobile R&D Center Hefei China

ISBN: (纸本)9783030309510

Although, semi-supervised learning with a small amount of labeled data can be utilized to improve the effectiveness of learning to rank in information retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring benefit for semi-supervised learning to rank is an excellent challenge. In this paper, we propose a semi-supervised learning to rank algorithm, that builds a query-quality predictor by utilizing uncertain data. Specially, this approach selects the training queries following the empirical observation that the relevant documents of high quality training queries are highly coherent. This approach learns from the uncertain data to predict the retrieval performance gain of a given training query by making use of query features. Then the pseudo labels for learning to rank are aggregated iteratively by semi-supervised learning with the selected queries. Experimental results on the standard LETOR dataset show that our proposed approaches outperform the strong baselines. © 2019, Springer Nature Switzerland AG.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Social Bot Detection via Structural Information Theory

arXiv

引用

arXiv 2024年

作者： Peng, Hao Zhang, Jingyun Huang, Xiang Hao, Zhifeng Li, Angsheng Yu, Zhengtao Yu, Philip S. School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China State Key Laboratory of Public Big Data Guizhou University No. 2708 South Section of Huaxi Avenue Huaxi District Guizhou Province Guiyang City550025 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China College of Science University of Shantou No. 243 University Road Shantou515063 China School of Computer Science and Engineering Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

来源：评论

学校读者我要写书评

暂无评论

The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning to Reweight Samples with Offline Loss Sequence

Learning to Reweight Samples with Offline Loss Sequence

引用

IEEE International Conference on data Mining (ICDM)

作者： Yuhua Wei Xiaoyu Li Jishang Wei Buyue Qian Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an China HP Labs 1501 Page Mill Rd Palo Alto CA USA The First Affiliated Hospital of Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665423991

Deep neural networks (DNNs) provide the best of class solutions to many supervised tasks due to their powerful function fitting capabilities. However, it is challenging to handle data bias, such as label noise and class imbalance, when applying DNNs to solve real-world problems. Sample reweighting is a popular strategy to tackle data bias, which assigns higher weights to informative samples or samples with clean labels. However, conventional reweighting methods require prior knowledge of the distribution information of data bias, which is intractable in practice. In recent years, meta-learning-based methods have been proposed to learn to assign weights to training samples adaptively by using their online training loss or gradient directions. However, the latent bias distribution cannot be adequately characterized in an online fashion. The online loss distribution changes over the training procedure, making it even harder to perform the sample weight learning. In contrast to past methods, we propose a two-stage training strategy to tackle the above problems. In the first stage, the loss sequences of samples are collected. In the second stage, a subnet with convolutional layers is utilized to learn the mapping from offline sample loss sequence to sample weight adaptively. Guided by a small unbiased meta dataset, this subnet is optimized iteratively with the main classifier network in a meta-learning manner. Empirical results show that our method, called Meta Reweighting with Offline Loss Sequence (MROLS), outperforms state-of-the-art reweighting techniques on most benchmarks. Moreover, the weights of training samples learned via MROLS can be well utilized by other classifiers, which can directly enhance the standard training schema. Our source code is available at https://***/Neronjust2017/MROLS.

关键词： Training Convolutional codes Deep learning Conferences Neural networks Fitting Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012]

Franklin Open

引用

Franklin Open 2024年 9卷

作者： Xingyue Liu Kaibo Shi Kun Zhou Shiping Wen Yiqian Tang Lin Tang School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 PR China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 PR China Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 PR China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 PR China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

来源：评论

学校读者我要写书评

暂无评论

R2-Net: Relation of relation learning network for sentence semantic matching

arXiv

引用

arXiv 2020年

作者： Zhang, Kun Wu, Le Lv, Guangyi Wang, Meng Chen, Enhong Ruan, Shulan School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches. © 2020, CC BY.

关键词： Natural language processing systems

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：