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Environmental Monitoring Using Autonomous Aquatic Robots: sampling algorithms and Experiments

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2013年第3期21卷 899-905页

作者： Jadaliha, Mahdi Choi, Jongeun Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

This brief presents a practical solution to the problem of monitoring an environmental process in a large region by a small number of robotic sensors. Optimal sampling strategies are developed, taking into account the quality of the estimated environmental field and the lifetime of the sensors. We also present experimental results for monitoring a temperature field of an outdoor swimming pool sampled by an autonomous aquatic surface robot. Simulation and experimental results are provided to validate the proposed scheme.

关键词： Environmental monitoring Kalman filter (KF) mobile sensor networks sampling algorithms

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Social Choice Around Decentralized Autonomous Organizations: On the Computational Social Choice of Digital Communities 23

Social Choice Around Decentralized Autonomous Organizations:...

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Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

作者： Nimrod Talmon Ben-Gurion University Be'er Sheva Israel

ISBN: (纸本)9781450394321

Decentralized Autonomous Organizations (DAOs) are sovereign digital communities that are owned by their members and that are algorithmically-controlled, usually by encoding their rules of conduct as smart contracts. Even though such communities become more popular and influential, their governance capabilities are still limited and lacking in quality. We argue that the MAS community holds the keys to improving the governance capabilities of DAOs; and that the challenge of DAO governance constitutes an important, new application area for MAS research that has the potential to have both scientific and societal impacts. Concretely, we describe DAOs and their governance needs and highlight gaps between the state of the art of MAS research and the governance needs of DAOs.

关键词： decentralized autonomous organization sampling algorithms continuous optimization computational social choice coalition formation prediction markets liquid democracy

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Mental models, computational explanation and Bayesian cognitive science: commentary on Knauff and Gazzo Castaneda (2022)

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THINKING & REASONING 2023年第3期29卷 371-382页

作者： Oaksford, Mike Univ London Birkbeck Coll London England

Knauff and Gazzo Castaneda (2022) object to using the term "new paradigm" to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term "paradigm" may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved.

关键词： Mental models Bayesian cognitive science sampling algorithms

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Efficient anomaly detection on sampled data streams with contaminated phase I data

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INTERNET TECHNOLOGY LETTERS 2020年第5期3卷

作者： El Sibai, Rayane Abdo, Jacques Bou Abou Jaoude, Chady Demerjian, Jacques Assaker, Joseph Makhoul, Abdallah Al Maaref Univ Fac Engn Beirut Lebanon Notre Dame Univ Fac Nat & Appl Sci Deir El Kamar Lebanon Antonine Univ Ticket Labaratory Fac Engn Baabda Lebanon Lebanese Univ LaRRIS Fac Sci Fanar Lebanon Univ Bourgogne Franche Comte UMR CNRS 6174 Femto ST Inst Besancon France

Control chart algorithms aim to monitor a process over time. This process consists of two phases. Phase I, also called the learning phase, estimates the normal process parameters, then in Phase II, anomalies are detected. However, the learning phase itself can contain contaminated data such as outliers. If left undetected, they can jeopardize the accuracy of the whole chart by affecting the computed parameters, which leads to faulty classifications and defective data analysis results. This problem becomes more severe when the analysis is done on a sample of the data rather than the whole data. To avoid such a situation, Phase I quality must be guaranteed. The purpose of this paper is to introduce a new approach for applying EWMA chart to obtain accurate anomaly detection results over sampled data even if contaminations exist in Phase I. The new chart is applied to a real dataset, and its performance is evaluated on both sampled and not sampled data according to several criteria.

关键词： anomalies detection data streams EWMA sampling algorithms

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Reliability increase of masonry characteristics estimation by a sampling algorithm applied to thermographic digital images

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PROBABILISTIC ENGINEERING MECHANICS 2020年 60卷 103022-103022页

作者： Cluni, Federico Costarelli, Danilo Gusella, Vittorio Vinti, Gianluca Univ Perugia Dept Civil & Environm Engn Via G Duranti 93 I-06125 Perugia Italy Univ Perugia Dept Math & Informat Via Vanvitelli 1 I-06123 Perugia Italy

In this paper the estimation of masonry characteristics by means of thermographic images, enhanced by sampling Kantorovich algorithm, is taken into account. In particular, the convergence of the Statistical Volume Element (SVE) to the Representative Volume Element (RVE) is analyzed. It is found that the enhancement, obtained by the proposed procedure, allows a faster convergence of SVE to RVE and a reduced coefficient of variation of the estimates obtained using a partition of the image with windows of smaller dimensions. Moreover, the effects of the uncertainties in the parameters involved in the reconstruction of texture from thermographic image, such as those used in morphological operators employed in digital image processing, involving the environmental conditions of the samples and the properties of the kernel utilized in the image enhancement algorithm, have been studied in order to assess their influence on the estimated mechanical characteristics. The performed analyses contribute to increase the reliability of the thermography as tool for identifying of masonries covered with plaster or frescoes, which is a very frequent case in vulnerability analysis of historical buildings.

关键词： Homogenization Masonry Thermography sampling algorithms Digital imaging

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Towards Efficient Data sampling for Temporal Anomaly Detection in Sensor Networks 2

Towards Efficient Data Sampling for Temporal Anomaly Detecti...

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2nd IEEE Middle East and North Africa COMMunications Conference (IEEE MENACOMM)

作者： El Sibai, Rayane Chabchoub, Yousra Abou Jaoude, Chady Demerjian, Jacques Togbe, Maurras ISEP LISITE F-92130 Issy Les Moulineaux France Antonine Univ Ticket Lab Fac Engn Baabda Lebanon Lebanese Univ LaRRIS Fac Sci Fanar Lebanon

ISBN: (纸本)9781728136875

Data streams sampling is intended to build a sample on which the future data analysis tasks will be performed. Several parameters affect the effectiveness of the built sample: the used sampling algorithm, chosen sampling rate, and window size if the sliding window model is adopted. Thus, given a stream of items, the most challenging task is to select the most relevant sampling technique to apply and the right parameters to employ to sample the data. In this paper, we address the impact of data sampling on the anomaly detection results. First, we develop a new version of the Weighted Random sampling (WRS) algorithm that samples the data based on their values with respect to the values of their neighbors in the current sliding window. Thereafter, we study the impact of the sampling process on the anomalies detection using the Exponential Weighted Moving Average (EWMA) algorithm. In this context, the comparison of the sampling algorithms is based on their response time in case of anomaly and the relevance of the detected anomalies.

关键词： Anomalies detection data streams EWMA sampling algorithms

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Geometric inhomogeneous random graphs

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THEORETICAL COMPUTER SCIENCE 2019年 760卷 35-54页

作者： Bringmann, Karl Keusch, Ralph Lengler, Johannes Max Planck Inst Informat Saarbrucken Germany Swiss Fed Inst Technol Inst Theoret Comp Sci Zurich Switzerland

Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. Instead of studying directly hyperbolic random graphs, we use a generalization that we call geometric inhomogeneous random graphs (GIRGs). Since we ignore constant factors in the edge probabilities, GIRGs are technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behavior of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by this new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a substantial factor O (root n). (2) We establish that GIRGs have clustering coefficients in Omega(1), (3) we prove that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Real-world networks Random graph models Hyperbolic random graphs sampling algorithms Compression algorithms Clustering coefficient

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sampling strategies for extracting information from large data sets

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DATA & KNOWLEDGE ENGINEERING 2018年 115卷 1-15页

作者： Boicea, Alexandru Truica, Ciprian-Octavian Radulescu, Florin Buse, Elena-Cristina Univ Politehn Bucuresti Fac Automat Control & Comp Dept Comp Sci & Engn Bucharest Romania

Getting information from large volumes of data is very expensive in terms of resources like CPU and memory, as well as computation time. The analysis of a small data set extracted from the original set is preferred. From this small set, called sample, approximate results can be obtained. The errors are acceptable given the reduced cost necessary for processing the data. Using sampling algorithms with small errors saves execution time and resources. This paper presents comparisons between sampling algorithms in order to determine which one performs better when taking into account set operations such as intersect, union and difference. The comparison focuses on the errors introduced by each algorithm for different sample sizes and on execution times.

关键词： sampling algorithms Space complexity Time complexity Set operations Data set cardinality Time optimization

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Imbalanced Learning for Cooperative Spectrum Sensing in Cognitive Radio Networks

Imbalanced Learning for Cooperative Spectrum Sensing in Cogn...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Li, Lusi Jiang, He He, Haibo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

ISBN: (纸本)9781728109626

We propose a novel cooperative spectrum sensing (CSS) framework for cognitive radio networks based on imbalanced learning techniques, which aims to resolve the skewed category distribution problems of signal data. For a radio channel shared by primary users (PUs) and secondary users (SUs), the signal data composed of energy vectors, in which each energy level is estimated by SU, can be used to detect the channel availability via a classifier. However, due to the nature of this application, the existing category-imbalance problem hinders the detection performance since the trained classifier has a better effect on the dominated category. To enhance the performance, sampling (e.g., oversampling, under-sampling, and combination) algorithms are employed to balance the training data set based on the imbalance degree metric of imbalance-ratio. The balanced training set then can be used to train classifiers with initial parameters, and the validation set can be utilized to tune as well as evaluate the classifiers. In the testing phase, the actual desired performance on unseen signal data can be determined based on the testing set, i.e., whether the channel is available or not. The performance of each sampling algorithm is measured in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The simulation results demonstrate the effectiveness of our proposed framework compared to traditional CSS methods.

关键词： Cognitive radio cooperative spectrum sensing imbalanced learning sampling algorithms detection performance

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Algorithmic Pirogov-Sinai Theory 2019

Algorithmic Pirogov-Sinai Theory

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51st Annual ACM SIGACT Symposium on Theory of Computing (STOC)

作者： Helmuth, Tyler Perkins, Will Regts, Guus Univ Bristol Bristol Avon England Univ Illinois Chicago IL USA Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450367059

We develop an efficient algorithmic approach for approximate counting and sampling in the low-temperature regime of a broad class of statistical physics models on finite subsets of the lattice Z(d) and on the torus (Z/nZ)(d). Our approach is based on combining contour representations from Pirogov-Sinai theory with Barvinok's approach to approximate counting using truncated Taylor series. Some consequences of our main results include an FPTAS for approximating the partition function of the hard-core model at sufficiently high fugacity on subsets of Z(d) with appropriate boundary conditions and an efficient sampling algorithm for the ferromagnetic Potts model on the discrete torus (Z/nZ)(d) at sufficiently low temperature.

关键词： Approximate counting algorithms Potts model Hard-core model statistical physics sampling algorithms Pirogov-Sinai theory

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