The Air Force released the Artificial Intelligence Strategy Sept. 12, highlighting the importance of artificial intelligence capabilities to 21st century missions. The strategy provides definition, context and purpose for artificial intelligence in the Air Force, and is the service's annex to the Department of Defense Artificial Intelligence Strategy. "The Air Force is charged to provide the nation with Air and Space Superiority, Global Strike, Rapid Global Mobility, Intelligence, Surveillance and Reconnaissance and Command Control," said Acting Secretary of the Air Force Matthew Donovan and Air Force Chief of Staff General David Goldfein in the dual-signed annex. "Al is a capability that will underpin our ability to compete, deter and win across all five of these diverse missions. It is crucial to fielding tomorrow's Air Force faster and smarter, executing multi-domain operations in the high-end fight, confronting threats below the level of open conflict and partnering with our allies around the globe." The strategy serves as the framework for aligning Air Force efforts with the National Defense Strategy and the Department of Defense Artificial Intelligence Strategy as executed by the Joint Artificial Intelligence Center.

Traditionally, off-policy learning algorithms (such as Q-learning) and exploration schemes have been derived separately. Often times, the exploration-exploitation dilemma being addressed through heuristics. In this article we show that both the learning equations and the exploration-exploitation strategy can be derived in tandem as the solution to a unique and well-posed optimization problem whose minimization leads to the optimal value function. We present a new algorithm following this idea. The algorithm is of the gradient type (and therefore has good convergence properties even when used in conjunction with function approximators such as neural networks); it is off-policy; and it specifies both the update equations and the strategy to address the exploration-exploitation dilemma. To the best of our knowledge, this is the first algorithm that has these properties.

A new era in radioastronomy will begin with the upcoming large-scale surveys planned at the Australian Square Kilometre Array Pathfinder (ASKAP). ASKAP started its Early Science program in October 2017 and several target fields were observed during the array commissioning phase. The SCORPIO field was the first observed in the Galactic Plane in Band 1 (792-1032 MHz) using 15 commissioned antennas. The achieved sensitivity and large field of view already allow to discover new sources and survey thousands of existing ones with improved precision with respect to previous surveys. Data analysis is currently ongoing to deliver the first source catalogue. Given the increased scale of the data, source extraction and characterization, even in this Early Science phase, have to be carried out in a mostly automated way. This process presents significant challenges due to the presence of extended objects and diffuse emission close to the Galactic Plane. In this context we have extended and optimized a novel source finding tool, named CAESAR , to allow extraction of both compact and extended sources from radio maps. A number of developments have been done driven by the analysis of the SCORPIO map and in view of the future ASKAP Galactic Plane survey. The main goals are the improvement of algorithm performances and scalability as well as of software maintainability and usability within the radio community. In this paper we present the current status of CAESAR and report a first systematic characterization of its performance for both compact and extended sources using simulated maps. Future prospects are discussed in light of the obtained results.

In the time series classification domain, shapelets are small time series that are discriminative for a certain class. It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of datasets by taking as input distances from the input time series to different discriminative shapelets. Additionally, these shapelets can easily be visualized and thus possess an interpretable characteristic, making them very appealing in critical domains, such as the health care domain, where longitudinal data is ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based upon evolutionary computation. The advantages of the proposed approach are that (i) it is gradient-free, which could allow to escape from local optima more easily and to find suited candidates more easily and supports non-differentiable objectives, (ii) no brute-force search is required, which drastically reduces the computational complexity by several orders of magnitude, (iii) the total amount of shapelets and length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand, (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with less similar shapelets that result in similar predictive performances, and (v) discovered shapelets do not need to be a subsequence of the input time series. We present the results of experiments which validate the enumerated advantages.

Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this context, this work focus on a new method for color texture analysis considering all color channels in a more intrinsic approach. Our proposal consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel edges that cover spatial patterns throughout individual image color channels, while between-channel edges tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through a concise topological characterization of the modeled network in a multiscale approach with radially symmetric neighborhoods. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks, and also has the most stable performance between datasets, achieving $98.5(\pm1.1)$ of average accuracy against $97.1(\pm1.3)$ of MCND and $96.8(\pm3.2)$ of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, where results show that SSN also has higher performance and robustness.

While there are many examples in which robots provide social assistance, a lack of theory on how the robots should decide how to assist impedes progress in realizing these technologies. To address this deficiency, we propose a pair of computational models to guide a robot as it provides social assistance. The model of social autonomy helps a robot select an appropriate assistance that will help with the task at hand while also maintaining the autonomy of the person being assisted. The model of social alliance describes how a to determine whether the robot and the person being assisted are cooperatively working towards the same goal. Each of these models are rooted in social reasoning between people, and we describe here our ongoing work to adapt this social reasoning to human-robot interactions. Socially assistive robots (SARs) provide social assistance instead of physically intervening.

This work proposes the use of Bayesian approximations of uncertainty from deep learning in a robot planner, showing that this produces more cautious actions in safety-critical scenarios. The case study investigated is motivated by a setup where an aerial robot acts as a "scout" for a ground robot. This is useful when the below area is unknown or dangerous, with applications in space exploration, military, or search-and-rescue. Images taken from the aerial view are used to provide a less obstructed map to guide the navigation of the robot on the ground. Experiments are conducted using a deep learning semantic image segmentation, followed by a path planner based on the resulting cost map, to provide an empirical analysis of the proposed method. A comparison with similar approaches is presented to portray the usefulness of certain techniques, or variations within a technique, in similar experimental settings. The method is analyzed to assess the impact of variations in the uncertainty extraction, as well as the absence of an uncertainty metric, on the overall system with the use of a defined metric which measures surprise to the planner. The analysis is performed on multiple datasets, showing a similar trend of lower surprise when uncertainty information is incorporated in the planning, given threshold values of the hyperparameters in the uncertainty extraction have been met. We find that taking uncertainty into account leads to paths that could be 18% less risky on an average.

Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks. However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$. Traditional conditional approaches provide a model for a fixed set of covariates conditioned on another fixed set of observed covariates. Instead, in this work we develop a model that is capable of yielding all conditional distributions $p(x_u \mid x_o)$ (for arbitrary $x_u$) via tractable conditional likelihoods. We propose a novel extension of (change of variables based) flow generative models, arbitrary conditioning flow models (AC-Flow), that can be conditioned on arbitrary subsets of observed covariates, which was previously infeasible. We apply AC-Flow to the imputation of features, and also develop a unified platform for both multiple and single imputation by introducing an auxiliary objective that provides a principled single "best guess" for flow models. Extensive empirical evaluations show that our models achieve state-of-the-art performance in both single and multiple imputation across image inpainting and feature imputation in synthetic and real-world datasets. Code is available at https://github.com/lupalab/ACFlow.

Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predictions systems, and unhealthy social network environment. This study is related with the detection of fake and automated accounts which leads to fake engagement on Instagram. As far as we know, there is no publicly available dataset for fake and automated accounts. For this purpose, two datasets have been generated for the detection of fake and automated accounts. For the detection of these accounts, machine learning algorithms like Naive Bayes, logistic regression, support vector machines and neural networks are applied. Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is applied because of the unnatural bias in the dataset. To deal with the unevenness problem in the fake dataset, Smote-nc algorithm is implemented. For the automated and fake account detection problem, 86% and 96% are obtained, respectively.

SPOKEN SPEECH ENHANCEMENT USING EEG Gautam Krishna null Y an Han null Co Tran Mason Carnahan Ahmed H T ewfik Brain Machine Interface Lab, The University of Texas at Austin ABSTRACT In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model and Long short-term Memory (LSTM) regression based model. Our results demonstrate that EEG features can be used to clean speech recorded in presence of background noise. Index T erms -- electroencephalograpgy (EEG), speech enhancement, deep learning 1. INTRODUCTION Speech enhancement is the process of improving the quality of speech whose quality was degraded due to additive noise. Speech enhancement is a critical preprocessing method used to improve the performance of automatic speech recognition (ASR) systems operating in presence of background noise. Noisy speech is first fed into a speech enhancement system to produce enhanced speech which is then fed into the ASR model.