检索结果-内蒙古大学图书馆

作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Tasi, Meng-Hsiun Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market. © 2020 John Wiley & Sons Ltd

关键词： Forecasting

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Heavy hitters and the structure of local privacy

arXiv

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arXiv 2017年

作者： Bun, Mark Nelson, Jelani Stemmer, Uri Department of Computer Science Princeton University John A. Paulson School of Engineering and Applied Sciences Harvard University Dept. of Computer Science and Applied Math Weizmann Institute of Science

We present a new locally differentially private algorithm for the heavy hitters problem which achieves optimal worst-case error as a function of all standardly considered parameters. Prior work obtained error rates which depend optimally on the number of users, the size of the domain, and the privacy parameter, but depend sub-optimally on the failure probability. We strengthen existing lower bounds on the error to incorporate the failure probability, and show that our new upper bound is tight with respect to this parameter as well. Our lower bound is based on a new understanding of the structure of locally private protocols. We further develop these ideas to obtain the following general results beyond heavy hitters. Advanced Grouposition: In the local model, group privacy for k users degrades proportionally to ≈ √k, instead of linearly in k as in the central model. Stronger group privacy yields improved max-information guarantees, as well as stronger lower bounds (via "packing arguments"), over the central model. Building on a transformation of Bassily and Smith (STOC 2015), we give a generic transformation from any non-interactive approximate-private local protocol into a pure-private local protocol. Again in contrast with the central model, this shows that we cannot obtain more accurate algorithms by moving from pure to approximate local privacy. Copyright © 2017, The Authors. All rights reserved.

关键词： Errors

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Identification of Static Structures of Reusable Software Architectures

Identification of Static Structures of Reusable Software Arc...

引用

IEEE Southeastcon

作者： E.H. Dennis E.K. Mugisa Math & Computer Science Department Faculty of Pure and Applied Sciences University of the West Indies Jamaica

Reusable software architecture (RSA) is playing an integral role in Component-Based Software Development (CBSD). This should assist productivity and increase software quality. This work looks at the identification of the key factors that will assist with the development of an RSA. The work involves a formal specification of an RSA and its structures.

关键词： Software reusability computer architecture Software architecture Formal specifications Programming Productivity Software quality Software systems Unified modeling language computer science

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Optimal early stopping in distributed consensus 6th

Optimal early stopping in distributed consensus

引用

6th International Workshop on Distributed Algorithms, WDAG 1992

作者： Berman, Piotr Garay, Juan A. Perry, Kenneth J. Department of Computer Science The Pennsylvania State University University ParkPA16802 United States Dept. of Applied Math and Computer Science The Weizmann Institute of Science Rehovot76100 Israel IBM T.J. Watson Research Center P.O. Box 704 Yorktown HeightsNY10598 United States

ISBN: (纸本)9783540561880

The Distributed Consensus problem involves n processors each of which holds an initial binary value. At most t processors may be faulty and ignore any protocol (even behaving maliciously), yet it is required that the non-faulty processors eventually agree on a value that was initially held by one of them. This paper presents consensus protocols that tolerate arbitrary faults, are early-stopping (i.e., run for a number of rounds proportional to the number of faults f that actually occur during their execution), and are optimal in various measures. Our first contribution is an early-stopping consensus protocol that is simultaneously optimal in round complexity (i.e., min(t+1, f+2) rounds) and number of processors (i.e., n > 3t). This settles a long-standing open question [DRS, 1982]. These bounds were not known to be attainable even with the use of authentication. Since consensus is not attainable with n ≤ 3t we provide a definitive answer to the problem of early-stopping consensus. Previous protocols for this problem that achieved round optimality required n = Ω(t2) [C, DRS], n > 6t [MW] and n > 4t [BGP]. Instrumental in achieving this result is the new safe message reconstruction technique, which we expect to be of broader applicability. The previous protocol is round- and processor-optimal, but not efficient. Our second contribution is a pair of optimal early-stopping consensus protocols that use messages of constant size. No previously existing early-stopping protocol, whether exactly or only asymptotically optimal in some measures, has used messages of constant size. The only other existing early-stopping protocol with constant-sized messages requires n = Ω(t2) [C]. Finally, we indicate how to extend one of the previous protocols to be optimal in total bit (and message) complexity and number of processors. This is the first protocol that is both optimal in these measures and early-stopping. © 1992, Springer Verlag. All rights reserved.

关键词： Artificial intelligence

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PCPs via Low-Degree Long Code and Hardness for Constrained Hypergraph Coloring

PCPs via Low-Degree Long Code and Hardness for Constrained H...

引用

Annual IEEE Symposium on Foundations of computer science

作者： Irit Dinur Venkatesan Guruswami Dept. of Applied Math and Computer Science The Weizmann Institute of Science Rehovot Israel Computer Science Department Carnegie Mellon University Pittsburgh USA

We develop new techniques to incorporate the recently proposed “short code” (a low-degree version of the long code) into the construction and analysis of PCPs in the classical “Label Cover + Fourier Analysis” framework. As a result, we obtain more size-efficient PCPs that yield improved hardness results for approximating CSPs and certain coloringtype problems. In particular, we show a hardness for a variant of hypergraph coloring (with hyperedges of size 6), with a gap between 2 and exp(2 Ω (√log log N)) number of colors where N is the number of vertices. This is the first hardness result to go beyond the O(log N) barrier for a coloring-type problem. Our hardness bound is a doubly exponential improvement over the previously known O(log log N)-coloring hardness for 2-colorable hypergraphs, and an exponential improvement over the (logN) Ω(1) -coloring hardness for O(1)-colorable hypergraphs. Stated in terms of “covering complexity,” we show that for 6-ary Boolean CSPs, it is hard to decide if a given instance is perfectly satisfiable or if it requires more than 2Ω(√log log N) assignments for covering all of the constraints. While our methods do not yield a result for conventional hypergraph coloring due to some technical reasons, we also prove hardness of (log N) Ω(1) -coloring 2-colorable 6-uniform hypergraphs (this result relies just on the long code). A key algebraic result driving our analysis concerns a very low-soundness error testing method for Reed-Muller codes. We prove that if a function β : F 2 m → F 2 is 2 Ω(d) far in absolute distance from polynomials of degree m-d, then the probability that deg(βg) ≤ m-3d/4 for a random degree d/4 polynomial g is doubly exponentially small in d.

关键词： Frequency modulation Polynomials Color Testing Approximation methods Noise computer science

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Lower bounds for cutting planes proofs with small coefficients 95

Lower bounds for cutting planes proofs with small coefficien...

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Proceedings of the twenty-seventh annual ACM symposium on Theory of computing

作者： Maria Bonet Toniann Pitassi Ran Raz Department of Mathematics University of Pennsylvania Mathematics and Computer Science University of Pittsburgh Department of Applied Math Weizmann Institute

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A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

arXiv

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arXiv 2020年

作者： Popov, Andrey A. Sandu, Adrian Nino-Ruiz, Elias D. Evensen, Geir Computational Science Laboratory Department of Computer Science Virginia Tech United States Applied Math and Computer Science Lab Universidad del Norte Colombia Norway

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation. We additionally provide for a way in which this methodology can be localized similar to the state-of-the-art LETKF method, and that for a certain model setup, our methodology significantly outperforms it. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

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Co-clustering of image segments using convex optimization applied to EM neuronal reconstruction

Co-clustering of image segments using convex optimization ap...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shiv N. Vitaladevuni Ronen Basri Howard Hughes Medical Institute USA Department of Computer Science and Applied Math. Weizmann Institute of Science Israel

This paper addresses the problem of jointly clustering two segmentations of closely correlated images. We focus in particular on the application of reconstructing neuronal structures in over-segmented electron microscopy images. We formulate the problem of co-clustering as a quadratic semi-assignment problem and investigate convex relaxations using semidefinite and linear programming. We further introduce a linear programming method with manageable number of constraints and present an approach for learning the cost function. Our method increases computational efficiency by orders of magnitude while maintaining accuracy, automatically finds the optimal number of clusters, and empirically tends to produce binary assignment solutions. We illustrate our approach in simulations and in experiments with real EM data.

关键词： Image segmentation Image reconstruction Linear programming Electron microscopy Cost function Biomedical imaging computer science Application software Computational efficiency Computational modeling

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