检索结果-内蒙古大学图书馆

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Alexander G. Reisach Christof Seiler Sebastian Weichwald Department of Mathematical Sciences University of Copenhagen Denmark and Department of Data Science and Knowledge Engineering Maastricht University The Netherlands Department of Data Science and Knowledge Engineering Maastricht University The Netherlands and Mathematics Centre Maastricht Maastricht University The Netherlands Department of Mathematical Sciences University of Copenhagen Denmark

ISBN: (纸本)9781713845393

Simulated DAG models may exhibit properties that, perhaps inadvertently, render their structure identifiable and unexpectedly affect structure learning algorithms. Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models. We introduce varsortability as a measure of the agreement between the order of increasing marginal variance and the causal order. For commonly sampled graphs and model parameters, we show that the remarkable performance of some continuous structure learning algorithms can be explained by high varsortability and matched by a simple baseline method. Yet, this performance may not transfer to real-world data where varsortability may be moderate or dependent on the choice of measurement scales. On standardized data, the same algorithms fail to identify the ground-truth DAG or its Markov equivalence class. While standardization removes the pattern in marginal variance, we show that data generating processes that incur high varsortability also leave a distinct covariance pattern that may be exploited even after standardization. Our findings challenge the significance of generic benchmarks with independently drawn parameters.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Two novel semantics of top-k queries processing in uncertain database

Two novel semantics of top-k queries processing in uncertain...

引用

10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Liu, Dexi Wan, Changxuan Xiong, N. Yang, Laurence T. Chen, Lei Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA United States Department of Computer Science St. Francis Xavier University Antigonish NS Canada Department of Computer Science HK University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9780769541082

Top-k query is a powerful technique in uncertain databases because of the existence of exponential possible worlds, and it is necessary to combine score and confidence of tuples to derive top k answers. Different semantics, the combination methods of score and confidence, lead to different results. U-kRanks and Global Top-k are two semantics of Topk queries in uncertain database, which consider every alternative in x-tuple as single one and return the tuple which has the highest probability appearing at top k or a given rank. However, no matter which alternative (tuple) of an x-tuple appears in a possible world, it undoubtedly believes that this xtuple appears in the same possible world accordingly. Thus, instead of ranking every individual tuple, we define two novel Top-k queries semantics in uncertain database, Uncertain xkRanks queries (U-x-kRanks) and Global x-Top-k queries (Gx- Top-k), which return k entities according to the score and the confidence of alternatives in x-tuple, respectively. In order to reduce the search space, we present an efficient algorithm to process U-x-kRanks queries and G-x-Top-k queries. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. © 2010 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Injecting Discrimination and Privacy Awareness Into Pattern Discovery

Injecting Discrimination and Privacy Awareness Into Pattern ...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Sara Hajian Anna Monreale Dino Pedreschi Josep Domingo-Ferrer Fosca Giannotti Department of Computer Engineering and Math. Universitat Rovira i Virgili Department of Computer Science Knowledge Discovery and Data Mining Lab ISTI-CNR Knowledge Discovery and Data Mining Lab Pisa

data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.

关键词： data privacy Privacy Additives Context Itemsets data models

来源：评论

学校读者我要写书评

暂无评论

A comparison of content-based tag recommendations in folksonomy systems

引用

Lecture Notes in Computer science

作者： Illig, Jens Hotho, Andreas Jäschke, Robert Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 34121 Kassel Germany Research Center L3S Appelstraße 9a 30167 Hannover Germany

ISBN: (纸本)9783642221392

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. © 2011 Springer-Verlag.

关键词： Computers

来源：评论

学校读者我要写书评

暂无评论

Body-Surface Atrial Vector Similarity as a New Way to Investigate Atrial Fibrillation Propagation Dynamics

Body-Surface Atrial Vector Similarity as a New Way to Invest...

引用

2021 Computing in Cardiology, CinC 2021

作者： Bonizzi, Pietro Zeemering, Stef Karel, Joel Lankveld, Theo Schotten, Ulrich Crijns, Harry Peeters, Ralf Meste, Olivier Maastricht University Department of Data Science and Knowledge Engineering Netherlands Department of Physiology Maastricht University Medical Center Netherlands Maastricht University Netherlands Department of Cardiology Maastricht University Medical Center Netherlands Universite Cote D'Azur CNRS I3S France

ISBN: (纸本)9781665479165

Atrial propagation patterns during atrial fibrillation (AF) can be characterized by a certain degree of recurrence (associated with different types of reentrant circuits that can drive the arrhythmia). In this study, we investigated this recurrent activity at the level of the body-surface, by measuring the level of similarity between pairs of consecutive atrial vectors. High-density body surface potential maps (120 anterior, 64 posterior electrodes) were recorded in 75 patients in persistent AF. For each patient, atrial vectors were created by taking the samples from all electrodes at each time instant. Similarity between consecutive vectors was measured in terms of the value of the cosine of the angle between two vectors. In all patients, the series of cosine values showed a quasi-periodic behavior, with atrial vectors alternating between phases of slow motion, and phases of fast motion. Moreover, the frequency of this behavior is about twice the AF dominant frequency, which suggests that within one AF cycle there are two phases of slow motion and two of fast motion, alternating. Finally, the amount of slow phases is positively correlated with a higher long-term recurrent behavior of the atrial propagation patterns. This seems to indicate that atrial vectors may provide a new way to noninvasively investigate atrial fibrillation dynamics. © 2021 Creative Commons.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Query-Oriented Routing Indices in Unstructured Peer-to-Peer Networks

Reinforcement Learning for Query-Oriented Routing Indices in...

引用

International Conference on Peer-to-Peer Computing

作者： Cong Shi Shicong Meng Yuanjie Liu Dingyi Han Yong Yu Apex Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves routing efficiency with no excessive network overhead of the routing index construction and maintenance. However, the previously proposed mechanism is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel mechanism for query-oriented routing indices which quickly achieves high routing efficiency at low cost. The maintenance method employs reinforcement learning to utilize mass peer behaviors to construct and maintain routing indices. It explicitly uses the expected value of returned content number to depict the content distribution, which helps quickly approximate the real distribution. Meanwhile, the routing method is to retrieve as many contents as possible. It also helps speed up the learning process further. The experimental evaluation shows that the mechanism has high routing efficiency, quick learning ability and satisfactory performance under churn

关键词： Learning Routing Peer to peer computing Query processing Buildings knowledge management Computer science data engineering knowledge engineering Costs

来源：评论

学校读者我要写书评

暂无评论

Query Minimization Under Stochastic Uncertainty 14th

Query Minimization Under Stochastic Uncertainty

引用

14th Latin American Symposium on Theoretical Informatics, LATIN 2020

作者： Chaplick, Steven Halldórsson, Magnús M. de Lima, Murilo S. Tonoyan, Tigran Lehrstuhl für Informatik I Universität Würzburg Würzburg Germany Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands ICE-TCS Department of Computer Science Reykjavik University Reykjavik Iceland School of Informatics University of Leicester Leicester United Kingdom Computer Science Department Technion Institute of Technology Haifa Israel

ISBN: (纸本)9783030617912

We study problems with stochastic uncertainty data on intervals for which the precise value can be queried by paying a cost. The goal is to devise an adaptive decision tree to find a correct solution to the problem in consideration while minimizing the expected total query cost. We show that sorting in this scenario can be performed in polynomial time, while finding the data item with minimum value seems to be hard. This contradicts intuition, since the minimum problem is easier both in the online setting with adversarial inputs and in the offline verification setting. However, the stochastic assumption can be leveraged to beat both deterministic and randomized approximation lower bounds for the online setting. Although some literature has been devoted to minimizing query/probing costs when solving uncertainty problems with stochastic input, none of them have considered the setting we describe. Our approach is closer to the study of query-competitive algorithms, and it gives a better perspective on the impact of the stochastic assumption. © 2020, Springer Nature Switzerland AG.

关键词： Sorting

来源：评论

学校读者我要写书评

暂无评论

Transfer of fully convolutional policy-value networks between games and game variants

arXiv

引用

arXiv 2021年

作者： Soemers, Dennis J.N.J. Mella, Vegard Piette, Éric Stephenson, Matthew Browne, Cameron Teytaud, Olivier Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Facebook AI Research Paris France

In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games. We explore how to transfer trained parameters of these architectures based on shared semantics of channels in the state and action representations of the Ludii general game system. We use Ludii’s large library of games and game variants for extensive transfer learning evaluations, in zero-shot transfer experiments as well as experiments with additional fine-tuning time. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Mining and Predicting Duplication over Peer-to-Peer Query Streams

Mining and Predicting Duplication over Peer-to-Peer Query St...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Shicong Meng Yifeng Shao Cong Shi Dingyi Han Yong Yu APEX Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Many previous works of data mining user queries in peer-to-peer systems focused their attention on the distribution of query contents. However, few has been done towards a better understanding of the time series distribution of these queries, which is vital for system performance. To remedy this situation, this paper mines query steams by using automatic time series analysis to evaluate different linear models (Box-Jenkins models and some simple windowed-mean models) for predicting the number of duplicated queries from 10 minutes to 2 hours into the future. Both the predictive power and the computational costs of these models are evaluated over 318,942,450 real world Gnutella queries collected over 3 months. We find the number of duplicated queries is consistently predictable. Simple, practical models like AR perform well on prediction

关键词： Peer to peer computing Predictive models Power system modeling data mining System performance Crawlers Time series analysis Computational efficiency Load management knowledge management

来源：评论

学校读者我要写书评

暂无评论

CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

引用

39th IEEE International Conference on data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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：