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作者： Xie, L AUSTRALIAN NATIONAL UNIVERSITY RESEARCH OFFICE ACTON Asian Office of Aerospace Research and Development (AOARD) Tokyo Asian Office of Aerospace Research and Development Tokyo JP (AOARD)

This project develops methods and models for understanding and predicting attention dynamics across online platforms. The project consists of five successive topics focusing on measurements, data sampling, and predictive models for social processes. First, we showideological asymmetries in digital space and provide a set of methods to quantify attention dynamics across different social platforms, especially YouTube and Twitter on long-running controversial topics. Second, we measure the correlation between online behavior and offline attitudes and actions, grounded on the theory of discursive opportunities. Third, we present a first study on cross-partisan communications on YouTube comments and find that the crosstalk is not symmetric. Fourth, we present a first study on measurement errors under subsampled Twitter data streams, and discuss noises and potential biases in social data. Lastly, we develop three different models that explain how social process unfolds: a mathematical relationship between self-exciting processes and stochastic epidemic models; a succinct neural model that universally approximates any point process; and a dual mixture model that is particularly suited to long-tailed data with both popular and unpopular content.

关键词： Information science Social media Data mining Information processing Information systems Knowledge management Kernel functions Neural networks Recurrent neural networks Machine learning Surveys algorithms Probability Reliability statistical algorithms Theorems Data science

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Air Force Personnel Center Best Practices Guide: Test Development and Validation

Air Force Personnel Center Best Practices Guide: Test Develo...

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作者： LeBreton, J. M PDRI Arlington VA 711 Human Performance Wing Wright-Patterson AFB OH 711th Human Performance Wing Wright-Patterson AFB OH

This series of reports consolidates the experience, wisdom, and tools the Air Force has accumulated in its selection and classification work, and blends them with best practice recommendations from industry. This entry covers test development and validation, beginning with an introduction to AFPC/DSYX and providing recommendations and best practices around test development and validation in the Air Force. The recommendations are based on over a century of scientific research and practice, both within the United States Air Force (USAF) and in the scientific literature more generally.

关键词： Information processing Information science Surveys Test and evaluation Factor analysis Information systems Knowledge management Psychology Data science Employment statistical algorithms Air force Psychological tests Management personnel Business administration Job analysis Processing equipment Regression analysis Air force personnel Personnel management

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Wavelet Methods for Very-short Term Forecasting of Functional Time Series

Wavelet Methods for Very-short Term Forecasting of Functiona...

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作者： Nystrom, J. K AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH

Space launch operations at Kennedy Space Center and Cape Canaveral Space Force Station (KSC/CCSFS) are complicated by unique requirements for near-real time determination of risk from lightning. Lightning forecast weather sensor networks produce data that are noisy, high volume, and high frequency time series for which traditional forecasting methods are often ill-suited. Current approaches result in significant residual uncertainties and consequentially may result in forecasting operational policies that are excessively conservative or inefficient. This work proposes a new methodology of wavelet-enabled semiparametric modeling to develop accurate and timely forecasts robust against chaotic functional data. Wavelets methods are first used to de-noise the weather data, which is then used to estimate a single-index model for forecasting. This semiparametric technique mitigates noise of the chaotic signal while avoiding any possible distributional misspecification.

关键词： Computational science Information science Neural networks Data mining Dimensionality reduction Experimental design Data science Knowledge management Surveys Machine learning statistical algorithms Supervised machine learning Detectors Information processing Sensor networks Air force Electromagnetic fields

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Quantum Computing algorithms For Decision Making Under Uncertainty

Quantum Computing Algorithms For Decision Making Under Uncer...

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作者： Horesh, L Clarkson, K Kalantzis, V Squillante, M Ubaru, S Abboud, A IBM Yorktown Heights NY Air Force Research Laboratory - Information Directorate Rome NY Air Force Research Laboratory Rome NY Information Directorate

The Quantum Computing algorithms fundamental research project focuses on the challenge of design of quantum computing algorithms viable for near-term noisy quantum systems. An emphasis was set on design of algorithms that avoid taking unrealistic assumptions while offering theoretical rigor regarding the expected computational advantage. The activity addressed three major development initiatives, namely: algorithmic design, implementation and testing, quantum computing application scoping, and data porting.

关键词： Quantum computing Computational science Quantum information Air force Machine learning Information science Quantum computers Quantum properties Data mining Information processing statistical algorithms Trees (data structures) Artificial intelligence Computational complexity Data analysis Monte carlo method Probability distributions

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NooJ Grammars and Ethical algorithms: Tackling On-Line Hate Speech 12th

NooJ Grammars and Ethical Algorithms: Tackling On-Line Hate ...

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12th International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ (NooJ)

作者： Monteleone, Mario Univ Salerno Dipartimento Sci Polit Sociali & Comunicaz Fisciano Italy

ISBN: (纸本)9783030108670;9783030108687

The definition of "on-line hate speech" covers all forms of expression that propagate, incite, promote or justify hatred based on intolerance, including that expressed in the form of discrimination and hostility against minorities. Moreover, the concept of hatred includes other sub-concepts such as Homophobia, Racism, Chauvinism, Terrorism, Nationalism, Tolerance/Intolerance, and so on. Specifically, on-line hate speech is used in cases of cyber-harassment, to harm others deliberately, repeatedly and aggressively, in a way so to weaken victims psychologically. To contrast this phenomenon, EC has allocated a relevant amount of H2020 funds for the completion of specific research projects, the goal of which is the construction of computer tools to locate, evaluate and eventually block on-line hate speech. Today, the automatic tackling of online hatred is a daily-performed operation on Social Forums like Facebook, Twitter and Instagram. However, the algorithms these Social Forums use are stochastic/statistical, therefore not suitable to contextualize syntactically and semantically the words used inside posts. Therefore, with on-line hate speech tackling, statistical algorithms may produce inaccurate or even false results, with rather serious consequences.

关键词： NooJ NooJ finite-state automata/transducers On-line hate speech Ethical algorithms Rule-based algorithms statistical algorithms

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Study of Naval Wholesale Inventory Optimization Model

Study of Naval Wholesale Inventory Optimization Model

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作者： Hague, A Naval Postgraduate School Monterey CA

Navy Supply Systems Command manages a vast inventory of items to meet the fleets requirements. A requisition decision for restocking an item is made, in part, based on the planned minimum safety stock (PMSS) level. The Wholesale Inventory Optimization Model with Enterprise Resource Planning (WIOM-ERP) simulates the fleets requirement demand based on historic data to establish metrics that serve as the basis to recommend the PMSS levels. NAVSUP uses WIOM-ERP to balance its inventory between procurement persistence (avoidance of "churn") and fulfillment of demand (fill rate). WIOM-ERP provides the flexibility for planners to weigh the importance of each metric and shift the balance between churn and fill rate. Decision makers seek insight into the relative tradeoff between churn and fill rate, resulting from changing the weight of each metric. This study applies a high-dimensional statistical design of experiments to simulate different combinations of metric weights for the WIOM-ERP user inputs. The output of WIOM-ERP is recorded for each design point to calculate measures of performance (MOPs) for churn and fill rate. The data are analyzed by developing statistical metamodels for each MOP to determine the impact on the metrics of the factors, their interactions, and the resulting tradeoffs between different combinations of weights.

关键词： Databases Department of defense Ruby programming language Supply chain Experimental design Information science Operations research statistical algorithms Data science Regression analysis Simulations United states B band Blood coagulation factors C band California D band Factor analysis Literature surveys Mathematical programming

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Monte Carlo Simulation with Censored Sampling

Monte Carlo Simulation with Censored Sampling

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作者： Akin, E. W Naval Postgraduate School Monterey CA

We consider Monte Carlo simulation in a setting where the samples are subject to random censoring. Such censoring occurs in settings as varied and diverse as perimeter protection, survival analysis, and electro-magnetic spectrum monitoring. We introduce and analyze two estimators: one based on empirical likelihood methods and another rooted in control variates ideas. We show that the proposed estimators can dramatically reduce the estimator variance in relation to the crude Monte Carlo estimator while not sacrificing computational speed.

关键词： Information science Monte carlo method statistical algorithms Data science Maximum likelihood estimation Random variables Surveys Data mining Knowledge management Operations research Probability distributions statistical inference algorithms Estimators Numerical analysis Probability Sampling Statistics Theorems Computational complexity

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Artificial Intelligence Exploration (AIE) DARPA-PA-18-02-02 Artificial Intelligence Research Associate (AIRA)

Artificial Intelligence Exploration (AIE) DARPA-PA-18-02-02 ...

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作者： Garikipati, Krishna

We propose a Bayesian framework to develop new machine learning and operator inference methods to aid the discovery of physical phenomena and the prediction of material properties and responses. We specifically target the challenges in material physics associated with systematic attempts to a abstract complexity from a hierarchy of scales into predictive model forms and b delineate mechanisms of coupled materials physics. Our project develops the following tasks that unite artificial intelligence AI with the discovery of emergent physics I Scale bridging from quantum mechanics to continuum PDEs. 2 Physics discovery via system identification and operator inference. 3 Bayesian inference and uncertainty quantification for learning from data and quantifying predictive quality. 4 Optimal experimental design for intelligent data acquisition and management to achieve efficient high-level learning. The first two tasks address important application-specific problems and the next two tasks provide the AI interface.

关键词： neural networks computational science bayesian networks kalman filters information science machine learning information processing mathematical filters monte carlo method mechanics artificial intelligence artificial intelligence software bayesian inference data science statistical algorithms experimental design information systems materials acquisition algorithms

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Measurement investigations in tubular structures health monitoring via ultrasonic guided waves: A case of study

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MEASUREMENT 2019年 147卷 106800-000页

作者： Yaacoubi, Slah El Mountassir, Mahjoub Ferrari, Morgan Dahmene, Fethi Plateforme RDI CND Inst Soudure 4 Bvd Henri Becquerel F-57970 Yutz France

This paper deals with the health monitoring of tubular structures by means of Ultrasonic Guided Waves (UGW) technique. It investigates mainly the stability of measurements, which is a key topic in Structural Health Monitoring. A corrosion-like defect is machined in a full-scale tube and then its size is increased in five steps. Their cross-section areas (CSA) go from less than 1% to around 4.5%. To get a high accuracy, a 3D laser scanner was used to measure these CSAs. The influence of the probe/tube adhesion quality on defect detectability is investigated. Propagation mode type and central frequency are also taken into account. Environmental and operational conditions variations (EOC) are studied too. Regarding the obtained results, it can be concluded that the UGW are an efficient technique for the monitoring of tubes. However, to reach a high-performance level, statistical algorithms are needed to face the problem of EOC and then manage false alarms as reliable as possible. Furthermore, to make data more stable, special care should be paid to the implementation of the sensor, such as glue durability, bonding issues, etc. (C) 2019 Published by Elsevier Ltd.

关键词： Structural Health Monitoring Ultrasonic guided waves Measurements Defect detection Environmental and operational conditions statistical algorithms

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ON THE COMPLEXITY OF RANDOM SATISFIABILITY PROBLEMS WITH PLANTED SOLUTIONS

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SIAM JOURNAL ON COMPUTING 2018年第4期47卷 1294-1338页

作者： Feldman, Vitaly Perkins, Will Vempala, Santosh Google Brain Mountain View CA 94035 USA Univ Birmingham Sch Math Birmingham B15 2TT W Midlands England Georgia Tech Atlanta GA 30332 USA

The problem of identifying a planted assignment given a random k-satisfiability (k-SAT) formula consistent with the assignment exhibits a large algorithmic gap: while the planted solution becomes unique and can be identified given a formula with O(n log n) clauses, there are distributions over clauses for which the best-known efficient algorithms require n(k/2) clauses. We propose and study a unified model for planted k-SAT, which captures well-known special cases. An instance is described by a planted assignment sigma and a distribution on clauses with k literals. We define its distribution complexity as the largest r for which the distribution is not r-wise independent (1 <= r <= k for any distribution with a planted assignment). Our main result is an unconditional lower bound, tight up to logarithmic factors, for statistical (query) algorithms [M. Kearns, J. ACM, 45 (1998), pp. 983{1006;V. Feldman, E. Grigorescu, L. Reyzin, S. S. Vempala, and Y. Xiao, J. ACM, 64 (2017), pp. 8:1{8:37], matching known upper bounds, which, as we show, can be implemented using a statistical algorithm. Since known approaches for problems over distributions have statistical analogues (spectral, Markov Chain Monte Carlo, gradient-based, convex optimization, etc.), this lower bound provides a rigorous explanation of the observed algorithmic gap. The proof introduces a new general technique for the analysis of statistical query algorithms. It also points to a geometric paring phenomenon in the space of all planted assignments. We describe consequences of our lower bounds to Feige's refutation hypothesis [U. Feige, Proceedings of the ACM Symposium on Theory of Computing, 2002, pp. 534{543] and to lower bounds on general convex programs that solve planted k-SAT. Our bounds also extend to other planted k-CSP models and, in particular, provide concrete evidence for the security of Goldreich's one-way function and the associated pseudorandom generator when used with a sufficiently hard predica

关键词： planted satisfiability statistical algorithms k-SAT hypergraph partitioning

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