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检索条件"主题词=anytime algorithms"

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Matrix Profile XXX: MADRID: A Hyper -anytime and Parameter -Free Algorithm to Find Time Series Anomalies of all Lengths 23

Matrix Profile XXX: MADRID: A Hyper -Anytime and Parameter -...

引用

23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Lu, Yue Srinivas, Thirumalai Vinjamoor Akhil Nakamura, Takaaki Imamura, Makoto Keogh, Eamonn Univ Calif Riverside Riverside CA 92521 USA Mitsubishi Electr Corp Kamakura Kanagawa Japan Tokai Univ Tokyo Japan

ISBN: (纸本)9798350307887

In recent years there has been increasing evidence that one of the simplest time series anomaly detection methods, time series discords, remains one of the most effective methods. However, time series discords have one notable issue;the anomalies discovered depend on the algorithm's only input parameter, the subsequence length. The obvious way to bypass this issue is to find anomalies at every possible length, however this seems to be untenably slow. In this work we introduce MADRID, an algorithm to efficiently solve the all-discords problem. We show that we can reduce the absolute time to compute all-discords, and that by using a novel computation ordering strategy, MADRID is a Hyper-anytime Algorithm. We will formally define this term later, but this refers to an anytime algorithm that converges exceptionally fast. In practice this means that for most real-world analytical tasks, the user can interact with their data in real-time. The ability to compute anomalies of all lengths produces the issue of ranking anomalies of different lengths. We further introduce novel algorithms for this task. We demonstrate the utility of MADRID in various domains and show that it allows us to find anomalies that would otherwise escape our attention.

关键词： Time series anomaly detection anytime algorithms

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anytime Approximation in Probabilistic Databases via Scaled Dissociations 19

Anytime Approximation in Probabilistic Databases via Scaled ...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Van den Heuvel, Maarten Ivanov, Peter Gatterbauer, Wolfgang Geerts, Floris Theobald, Martin Univ Antwerp Antwerp Belgium Northeastern Univ Boston MA 02115 USA Univ Luxembourg Luxembourg Luxembourg

ISBN: (纸本)9781450356435

Speeding up probabilistic inference remains a key challenge in probabilistic databases (PDBs) and the related area of statistical relational learning (SRL). Since computing probabilities for query answers is #P-hard, even for fairly simple conjunctive queries, both the PDB and SRL communities have proposed a number of approximation techniques over the years. The two prevalent techniques are either (i) MCMC-style sampling or (ii) branch-and-bound (B&B) algorithms that iteratively improve model-based bounds using a combination of variable substitution and elimination. We propose a new anytime B&B approximation scheme that encompasses all prior model-based approximation schemes proposed in the PDB and SRL literature. Our approach relies on the novel idea of "scaled dissociation" which can improve both the upper and lower bounds of existing model-based algorithms. We apply our approach to the well-studied problem of evaluating self-join-free conjunctive queries over tuple-independent PDBs, and show a consistent reduction in approximation error in our experiments on TPC-H, Yago3, and a synthetic benchmark setting.

关键词： probabilistic databases approximate inference anytime algorithms query processing

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treeNets: A framework for anytime evaluation of belief networks 1st

treeNets: A framework for anytime evaluation of belief netwo...

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1st International Joint Conference on Qualitative and Quantitative Practical Reasoning (ECSQARU-FAPR 97)

作者： Jitnah, N Nicholson, A Monash Univ Dept Comp Sci Clayton Vic 3168 Australia

ISBN: (纸本)3540630953

We present a new framework for evaluation of belief networks (BNs). It consists of two steps: (1) transforming a belief network into a tree structure called a treeNet (2) performing anytime inference by searching the treeNet. The root of the treeNet represents the query node. Whenever new evidence is incorporated, the posterior probability of the query node is re-calculated, using a variation of the polytree message-passing algorithm. The treeNet framework is geared towards anytime evaluation. Evaluating the treeNet is a tree search problem and we investigate different tree search strategies. Using a best-first method, we can to increase the rate of convergence of the anytime result.

关键词： uncertainty Bayesian Networks anytime algorithms practical reasoning graphical models

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Determining Optimal Frame Processing Strategies for Real-Time Document Recognition Systems 16th

Determining Optimal Frame Processing Strategies for Real-Tim...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Bulatov, Konstantin Arlazarov, Vladimir V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia Smart Engines Serv LLC Moscow Russia

ISBN: (纸本)9783030863319

Mobile document analysis technologies became widespread and important, and growing reliance on the performance of critical processes, such as identity document data extraction and verification, lead to increasing speed and accuracy requirements. Camera-based documents recognition on mobile devices using video stream allows to achieve higher accuracy, however in real time systems the actual time of individual image processing needs to be taken into account, which it rarely is in the works on this subject. In this paper, a model of real-time document recognition system is described, and three frame processing strategies are evaluated, each consisting of a per-frame recognition results combination method and a dynamic stopping rule. The experimental evaluation shows that while full combination of all input results is preferable if the frame recognition time is comparable with frame acquisition time, the selection of one best frame based on an input quality predictor, or a combination of several best frames, with the corresponding stopping rule, allows to achieve higher mean recognition results accuracy if the cost of recognizing a frame is significantly higher than skipping it.

关键词： Mobile recognition Real-time recognition Video stream anytime algorithms Document recognition

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Towards Whole-Book Recognition

Towards Whole-Book Recognition

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8th International Workshop on Document Analysis Systems

作者： Xiu, Pingping Baird, Henry S. Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18017 USA

ISBN: (纸本)9780769533377

We describe experimental results for unsupervised recognition of the textual contents of book-images using fully automatic mutual-entropy-based model adaptation. Each experiment starts with approximate iconic and linguistic models-derived from (generally errorful) OCR results and (generally incomplete) dictionaries-and then runs a fully automatic adaptation algorithm which, guided entirely by evidence internal. to the test set, attempts to correct the models for improved accuracy The iconic model describes image formation and determines the behavior of a character-image classifier The linguistic model describes word-occurrence probabilities. Our adaptation algorithm detects disagreements between the models by analyzing mutual entropy between (1) the a posteriori probability distribution of character classes (the recognition results from image classification alone), and (2) the a posteriori probability distribution of word classes (the recognition results from image classification combined with linguistic constraints). Disagreements identify, candidates for automatic model corrections. We report experiments on 40 textlines in which word error rates fall monotonicaly with passage lengths. We also report experiments on an enhanced algorithm which can cope with character-segmentation errors (a single split, or a single merge, per word). In order to scale up experiments, soon, to whole book images, we have revised data structures and implemented speed enhancements. For this algorithm, we report results on three increasingly long passage lengths: (a) one full page, (b) five pages, and (b) ten pages. We observe that error rates on long words fall monotonically with passage lengths.

关键词： document image recognition book recognition isogeny adaptive classification anytime algorithms model adaptation mutual entropy

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Whole-book recognition using mutual-entropy-driven model adaptation

Whole-book recognition using mutual-entropy-driven model ada...

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Conference on Document Recognition and Retrieval XV

作者： Xiu, Pingping Baird, Henry S. Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18017 USA

ISBN: (纸本)9780819469878

We describe an approach to unsupervised high-accuracy recognition of the textual contents of an entire book using fully automatic mutual-entropy-based model adaptation. Given images of all the pages of a book together with approximate models of image formation (e.g. a character-image classifier) and linguistics (e.g. a word-occurrence probability model), we detect evidence for disagreements between the two models by analyzing the mutual entropy between two kinds of probability distributions: (1) the a posteriori probabilities of character classes (the recognition results from image classification alone), and (2) the a posteriori probabilities. of word classes (the recognition results from image classification combined with linguistic constraints). The most serious of these disagreements are identified as candidates for automatic corrections to one or the other of the models. We describe a formal information-theoretic framework for detecting model disagreement and for proposing corrections. We illustrate this approach on a small test case selected from real book-image data. This reveals that a sequence of automatic model corrections can drive improvements in both models, and can achieve a lower recognition error rate. The importance of considering the contents of the whole book is motivated by a series of studies, over the last decade, showing that isogeny can be exploited to achieve unsupervised improvements in recognition accuracy.

关键词： document image recognition book recognition isogeny adaptive classification anytime algorithms model adaptation mutual entropy

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A Modification of a Stopping Method for Text Recognition in a Video Stream with Best Frame Selection 13

A Modification of a Stopping Method for Text Recognition in ...

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13th International Conference on Machine Vision

作者： Tolstov, Ilya Martynov, Stanislav Farsobina, Vera Bulatov, Konstantin Russian Acad Sci Keldysh Inst Appl Math Moscow Russia Russian Acad Sci Fed Res Ctr Commuter Sci & Control Moscow Russia Smart Engines Serv LLC Moscow Russia

ISBN: (纸本)9781510640412

One of the most important problem in constructing computer vision systems for embedded and mobile devices is offline recognition of text strings. In this paper, we analyze the problem of text strings recognition process in a video stream using best frame selection. This method allows to incorporate the information from multiple views of the same target object, thus increasing the overall extraction accuracy. A stopping method is proposed, which allows to make an automatic stopping decision, i.e. to terminate the process at the optimal time in order to maximize the responsiveness of the system. Experimental evaluation on open identity document datasets MIDV-500 and MIDV-2019 show that the proposed stopping rule allows to decrease mean error level of the text recognition results in comparison with a baseline approach which stops after a fixed amount of processed frames.

关键词： mobile recognition video stream optical character recognition anytime algorithms optimal stopping

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Self-Adaptive anytime Stream Clustering

Self-Adaptive Anytime Stream Clustering

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9th IEEE International Conference on Data Mining

作者： Kranen, Philipp Assent, Ira Baldauf, Corinna Seidl, Thomas Rhein Westfal TH Aachen Aachen Germany Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781424452422

Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival time;of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.

关键词： stream clustering anytime algorithms self-adaptive algorithms

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Co-Design of anytime Computation and Robust Control 36

Co-Design of Anytime Computation and Robust Control

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IEEE 36th Real-Time Systems Symposium (RTSS)

作者： Pant, Yash Vardhan Mohta, Kartik Abbas, Houssam Nghiem, Truong X. Devietti, Joseph Mangharam, Rahul Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Penn Dept Comp & Informat Sci Philadelphia PA 19104 USA

ISBN: (纸本)9781467395076

Control software of autonomous robots has stringent real-time requirements that must be met to achieve the control objectives. One source of variability in the performance of a control system is the execution time and accuracy of the state estimator that provides the controller with state information. This estimator is typically perception-based (e.g., Computer Vision-based) and is computationally expensive. When the computational resources of the hardware platform become overloaded, the estimation delay can compromise control performance and even stability. In this paper, we define a framework for co-designing anytime estimation and control algorithms, in a manner that accounts for implementation issues like delays and inaccuracies. We construct an anytime perception-based estimator from standard off-the-shelf Computer Vision algorithms, and show how to obtain a trade-off curve for its delay vs estimate error behavior. We use this anytime estimator in a controller that can use this tradeoff curve at runtime to achieve its control objectives at a reduced energy cost. When the estimation delay is too large for correct operation, we provide an optimal manner in which the controller can use this curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing basic objectives are met. We illustrate our approach on an autonomous hexrotor and demonstrate its advantage over a system that does not exploit co-design.

关键词： anytime algorithms Co-design of computation and control Hexrotor Model Predictive Control Robust Controller Robust Switching Controller

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On a Control Algorithm for Time-varying Processor Availability

On a Control Algorithm for Time-varying Processor Availabili...

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13th International Conference on Hybrid Systems - Computation and Control

作者： Gupta, Vijay Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781605589558

We consider an anytime control algorithm for the situation when the processor resource availability is time-varying. The basic idea is to calculate the components of the control input vector sequentially to maximally utilize the processing resources available at every time step. Thus, the system evolves as a discrete time hybrid system with the particular mode active at any time step being dictated by the processor availability. We extend our earlier work to consider the sequence in which the control inputs are calculated as a variable. In particular, we propose stochastic decision rules in which the inputs are chosen according to a Markov chain. For the LQG case, we present a Markovian jump linear system based formulation that provides analytical performance and stability expressions. For more general cases, we present a receding horizon control based implementation and illustrate the increase in performance through simulations.

关键词： anytime algorithms Control

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