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ALOR: Adaptive layout optimization of raft groups for heterogeneous distributed key-value stores 1

15th IFIP International Conference on Network and Parallel Computing, NPC 2018

作者： Wang, Yangyang Chai, Yunpeng Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Many distributed key-value storage systems employ the simple and effective Raft protocol to ensure data consistency. They usually assume a homogeneous node hardware configuration for the underlying cluster and thus adopt even data distribution schemes. However, today’s distributed systems tend to be heterogeneous in nodes’ I/O devices due to the regular worn I/O device replacement and the emergence of expensive new storage media (e.g., non-volatile memory). In this paper, we propose a new data layout scheme called Adaptive Layout Optimization of Raft groups (ALOR), considering the hardware heterogeneity of the cluster. ALOR aims to optimize the data layout of Raft groups to achieve a better practical load balance, which leads to higher performance. ALOR consists of two components: leader migration in Raft groups and skewed data layout based on cold data migration. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively. © IFIP International Federation for Information Processing 2018.

关键词： Digital storage

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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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Bimagnetic Core/Shell Nanoprobes with Tunable Exchange Coupling for High Resolution and Sensitive Magnetic Particle Imaging

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Angewandte Chemie 2024年第6期137卷

作者： Qilong Li Changjian Li Yueqi Wang Miaomiao Li Zeyu Liang Qiyue Wang Bingzhe Wang Canyu Huang Jie Tian Fangyuan Li Prof. Daishun Ling Frontiers Science Center for Transformative Molecules School of Chemistry and Chemical Engineering School of Biomedical Engineering National Center for Translational Medicine Shanghai Jiao Tong University Shanghai 200240 China These authors contributed equally to this work School of Engineering Medicine Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine (Beihang University) Ministry of Industry and Information Technology Beijing 100191 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing 100190 China Clinical College of Armed Police General Hospital of Anhui Medical University Department of Gastroenterology of The Third Medical Center of Chinese PLA General Hospital Beijing 100039 China Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders (LEAD) Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai 201600 China World Laureates Association (WLA) Laboratories Shanghai 201203 China

Magnetic particle imaging (MPI) has demonstrated versatile applications in biomedicine, including tumor imaging, cell tracking, and image-guided hyperthermia. Despite these advancements, the prevalent use of clinically approved tracers has posed limitations on MPI′s resolution and sensitivity. In this study, we engineered a bimagnetic core/shell nanocrystals (BMCS) tailored for MPI by optimizing the heterostructure and modulating the exchange coupling effect between the two magnetic components. The resulting BMCS exhibited remarkably heightened susceptibility and magnetization while maintaining low coercivity, thereby substantially improved both MPI resolution and sensitivity compared to conventional tracers such as VivoTrax. At an equivalent mass concentration, BMCS demonstrated a notable 5.08-fold increase in signal intensity and achieved an unprecedentedly high resolution down to 1 mm. The excellent MPI performance contributes to high resolution MPI and the sensitive detection of orthotopic colorectal cancer in mice. The design strategy employed in BMCS, centered on the exchange coupling effect, introduces an efficacious approach for the development of high performance MPI tracers.

关键词： bimagnetic core/shell nanocrystals exchange coupling effect high resolution and sensitivity magnetic particle imaging

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Trajectory simplification: An experimental study and quality analysis 44th

Trajectory simplification: An experimental study and quality...

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44th International Conference on Very Large data Bases, VLDB 2018

作者： Zhang, Dongxiang Ding, Mengting Yang, Dingy Liu, Yi Fan, Ju Shen, Heng Tao Center for Future Media School of Computer Science and Engineering UESTC China School of Electronics and Information Shanghai Dian Ji University China The Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

The ubiquitousness of GPS sensors in smart-phones, vehicles and wearable devices has enabled the collection of massive volumes of trajectory data from tracing moving objects. Consequently, an unprecedented scale of timestamped GPS data has been generated and posed an urgent demand for an effective storage mechanism for trajectory databases. The mainstream compression technique is called trajectory simplification, that finds a subsequence to approximate the original trajectory and attempts to minimize the information loss under a distance measure. Even though various simplification algorithms have been proposed in the past decades, there still lacks a thorough comparison to cover all the state-of-the-art algorithms and evaluate their quality using datasets in diversified motion patterns. Hence, it still remains a challenge for GPS data collectors to determine a proper algorithm in a concrete application. In addition, almost the entire line of previous methods uses error-based metrics to evaluate the compression quality, while ignoring their usability in supporting spatio-temporal queries on top of the reduced database. To bridge these gaps, we conduct so far the most comprehensive evaluation on trajectory simplification techniques. We compare the performance of 25 algorithms in total using five real datasets in different motion patterns. According to the experimental findings, we present useful guidance for the selection or development of effective trajectory simplification algorithms. © VLDB Endowment 21508097/18/05

关键词： Trajectories

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Dual Encoding for Zero-Example Video Retrieval

Dual Encoding for Zero-Example Video Retrieval

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Jianfeng Dong Xirong Li Chaoxi Xu Shouling Ji Yuan He Gang Yang Xun Wang College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba Group

ISBN: (纸本)9781728132945

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval.

关键词： Videotapes video retrieval End users end-to-end Dual coding

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An Optimization-Based Truth Discovery Method with Claim Relation

An Optimization-Based Truth Discovery Method with Claim Rela...

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IEEE International Conference on Big knowledge (ICBK)

作者： Jiazhu Xia Ying He Yuxin Jin Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

With the advent of the era of big data, the information from multi-sources often conflicts due to that errors and fake information are inevitable. Therefore, how to obtain the most trustworthy or true information (i.e. truth) people need gradually becomes a troublesome problem. In order to meet this challenge, a novel hot technology named truth discovery that can infer the truth and estimate the reliability of the source without supervision has attracted more and more attention. However, most existing truth discovery methods only consider that the information is either same or different rather than the fine-grained relation between them, such as inclusion, support, mutual exclusion, etc. Actually, this situation frequently exists in real-world applications. To tackle the aforementioned issue, we propose a novel truth discovery method named OTDCR in this paper, which can handle the fine-grained relation between the information and infer the truth more effectively through modeling the relation. In addition, a novel method of processing abnormal values is applied to the preprocessing of truth discovery, which is specially designed for categorical data with the relation. Experiments in real dataset show our method is more effective than several outstanding methods.

关键词： Task analysis knowledge engineering Big data Crowdsourcing Reliability engineering Meteorology

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Ensemble Classification Method Based on Truth Discovery

Ensemble Classification Method Based on Truth Discovery

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Yuxin Jin Ze Yang Ying He Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

Classification is a hot topic in such fields as machine learning and data mining. The traditional approach of machine learning is to find a classifier closest to the real classification function, while ensemble classification is to integrate the results of base classifiers, then make an overall prediction. Compared to using a single classifier, ensemble classification can significantly improve the generalization of the learning system in most cases. However, the existing ensemble classification methods rarely consider the weight of the classifier, and there are few methods to consider updating the weights dynamically. In this paper, we are inspired by the idea of truth discovery and propose a new ensemble classification method based on the truth discovery (named ECTD). As far as we know, we are the first to apply the idea of truth discovery in the field of ensemble learning. Experimental results demonstrate that the proposed method performs well in ensemble classification.

关键词： Reliability Machine learning Classification algorithms Task analysis Buildings Computational modeling knowledge engineering

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Mining Dual Emotion for Fake News Detection

arXiv

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arXiv 2019年

作者： Zhang, Xueyao Cao, Juan Li, Xirong Sheng, Qiang Zhong, Lei Shu, Kai Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China Illinois Institute of Technology ChicagoIL United States

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features;2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.1 2 Copyright © 2019, The Authors. All rights reserved.

关键词： Fake detection

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Consensus with Voting Theory in Blockchain Environments

Consensus with Voting Theory in Blockchain Environments

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Li Yongkang Jiang Guanfeng Liu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Computing Department Macquarie University Sydney Australia

A blockchain can be taken as a decentralized and distributed public database. In order to achieve data consistency of the system nodes, the execution of a consensus algorithm is necessary and required in the case of decentralized environments. Simply speaking, the consensus is that every node agrees on some record in the blockchain. There are many kinds of consensus algorithms in blockchain environments, and each consensus algorithm has its own proper application scenario. Here we firstly analysis and compare various popular consensus algorithms in blockchain environments, and then as voting theory has systematically studied the decision-making in a group, the traditional methods of voting theory is summarized and listed, including (Position) scoring rules, Copeland, Maximin, Ranked pairs, Voting trees, Bucklin, Plurality with runoff, Single transferable vote, Baldwin rule, and Nanson rule. Finally, we introduce the voting methods from voting theory to consensus algorithms in the blockchain to improve its performance.

关键词： Blockchain Consensus algorithm Bitcoin Voting knowledge engineering Computer science Decision making

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Kernelized multiview subspace analysis by self-weighted learning

arXiv

引用

arXiv 2019年

作者： Wang, Huibing Wang, Yang Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Dalian Liaoning116021 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

—With the popularity of multimedia technology, information is always represented or transmitted from multiple views. Most of the existing algorithms are graph-based ones to learn the complex structures within multiview data but overlooked the information within data representations. Furthermore, many existing works treat multiple views discriminatively by introducing some hyperparameters, which is undesirable in practice. To this end, abundant multiview based methods have been proposed for dimension reduction. However, there are still no research to leverage the existing work into a unified framework. To address this issue, in this paper, we propose a general framework for multiview data dimension reduction, named Kernelized Multiview Subspace Analysis (KMSA). It directly handles the multi-view feature representation in the kernel space, which provides a feasible channel for direct manipulations on multiview data with different dimensions. Meanwhile, compared with those graph-based methods, KMSA can fully exploit information from multiview data with nothing to lose. Furthermore, since different views have different influences on KMSA, we propose a self-weighted strategy to treat different views discriminatively according to their contributions. A co-regularized term is proposed to promote the mutual learning from multi-views. KMSA combines self-weighted learning with the co-regularized term to learn appropriate weights for all views. We also discuss the influence of the parameters in KMSA regarding the weights of multi-views. We evaluate our proposed framework on 6 multiview datasets for classification and image retrieval. The experimental results validate the advantages of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Multimedia systems

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