Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the training and testing data of the learning models or can explore the workings of those models for launching advanced future attacks. The topic of adversarial security attacks and perturbations within the ML and DL domains is a recent exploration and a great interest is expressed by the security researchers and practitioners. The literature covers different adversarial security attacks and perturbations on ML and DL methods and those have their own presentation styles and merits. A need to review and consolidate knowledge that is comprehending of this increasingly focused and growing topic of research; however, is the current demand of the research communities. In this review paper, we specifically aim to target new researchers in the cybersecurity domain who may seek to acquire some basic knowledge on the machine learning and deep learning models and algorithms, as well as some of the relevant adversarial security attacks and perturbations.

Financial transactions can be considered edges in a heterogeneous graph between entities sending money and entities receiving money. For financial institutions, such a graph is likely large (with millions or billions of edges) while also sparsely connected. It becomes challenging to apply machine learning to such large and sparse graphs. Graph representation learning seeks to embed the nodes of a graph into a Euclidean vector space such that graph topological properties are preserved after the transformation. In this paper, we present a novel application of representation learning to bipartite graphs of credit card transactions in order to learn embeddings of account and merchant entities. Our framework is inspired by popular approaches in graph embeddings and is trained on two internal transaction datasets. This approach yields highly effective embeddings, as quantified by link prediction AUC and F1 score. Further, the resulting entity vectors retain intuitive semantic similarity that is explored through visualizations and other qualitative analyses. Finally, we show how these embeddings can be used as features in downstream machine learning business applications such as fraud detection.

Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that in general performing exact inference requires all of the data to be processed at each iteration of the algorithm. For large data sets, the computational cost of MCMC can be prohibitive, which has led to recent developments in scalable Monte Carlo algorithms that have a significantly lower computational cost than standard MCMC. In this paper, we focus on a particular class of scalable Monte Carlo algorithms, stochastic gradient Markov chain Monte Carlo (SGMCMC) which utilises data subsampling techniques to reduce the per-iteration cost of MCMC. We provide an introduction to some popular SGMCMC algorithms and review the supporting theoretical results, as well as comparing the efficiency of SGMCMC algorithms against MCMC on benchmark examples. The supporting R code is available online.

Recent introduction of ICT in agriculture has brought a number of changes in the way farming is done. This means use of Internet of Things(IoT), Cloud Computing(CC), Big Data (BD) and automation to gain better control over the process of farming. As the use of these technologies in farms has grown exponentially with massive data production, there is need to develop and use state-of-the-art tools in order to gain more insight from the data within reasonable time. In this paper, we present an initial understanding of Convolutional Neural Network (CNN), the recent architectures of state-of-the-art CNN and their underlying complexities. Then we propose a classification taxonomy tailored for agricultural application of CNN. Finally, we present a comprehensive review of research dedicated to applications of state-of-the-art CNNs in agricultural production systems. Our contribution is in two-fold. First, for end users of agricultural deep learning tools, our benchmarking finding can serve as a guide to selecting appropriate architecture to use. Second, for agricultural software developers of deep learning tools, our in-depth analysis explains the state-of-the-art CNN complexities and points out possible future directions to further optimize the running performance.

Cyber attackers currently focus most of their efforts on manipulating existing artificial intelligence (AI) systems for malicious purposes, instead of creating new attacks that use machine learning. That is the key finding of a report by the Sherpa consortium, an EU-funded project founded in 2018 to study the impact of AI on ethics and human rights, supported by 11 organisations in six countries, including the UK. However, the report notes that attackers have access to machine learning techniques, and AI-enabled cyber attacks will be a reality soon, according to Mikko Hypponen, chief research officer at IT security company F-Secure, a member of the Sherpa consortium. The continuing game of "cat and mouse" between attackers and defenders will reach a whole new level when both sides are using AI, said Hypponen, and defenders will have to adapt quickly as soon as they see the first AI-enabled attacks emerging. But despite the claims of some security suppliers, Hypponen told Computer Weekly in a recent interview that no criminal groups appear to be using AI to conduct cyber attacks.

As our world digitizes, information becomes more valuable. The excess of information and the rapid increase of the data causes the stored data to become polluted and unusable. I would like to start with an example in order to understand this science which is one of the new professions of the modern century more easily. Suppose that an automobile company that produces luxury sports cars is launching a new, very fast, single-door convertible, the company will naturally think who its potential customers are. Nerd IT staff working in the company have a new idea. He starts to make various analysis by getting information from market chains.

Last year we looked at'Relational inductive biases, deep learning, and graph networks,' where the authors made the case for deep learning with structured representations, which are naturally represented as graphs. Today's paper choice provides us with a broad sweep of the graph neural network landscape. It's a survey paper, so you'll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them. We'll be talking about graphs as defined by a tuple where is the set of nodes (vertices), is the set of edges, and A is the adjacency matrix. An edge is a pair, and the adjacency matrix is an (for N nodes) matrix where if nodes and are not directly connected by a edge, and some weight value 0 if they are.

The use of artificial intelligence (AI), cognitive technologies, and robotics to automate and augment work is on the rise, prompting the redesign of jobs in a growing number of domains. The jobs of today are more machine-powered and data-driven than in the past, and they also require more human skills in problem-solving, communication, interpretation, and design. As machines take over repeatable tasks and the work people do becomes less routine, many jobs will rapidly evolve into what we call "superjobs"--the newest job category that changes the landscape of how organizations think about work. During the last few years, many have been alarmed by studies predicting that AI and robotics will do away with jobs. In 2019, this topic remains very much a concern among our Global Human Capital Trends survey respondents.

This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI) classification. MPRI inherits the merits of the principle of relevant information (PRI) to effectively extract multiscale information embedded in the given data, and also takes advantage of the multilayer structure to learn representations in a coarse-to-fine manner. Specifically, MPRI performs spectral-spatial pixel characterization (using PRI) and feature dimensionality reduction (using regularized linear discriminant analysis) iteratively and successively. Extensive experiments on four benchmark data sets demonstrate that MPRI outperforms existing state-of-the-art HSI classification methods (including deep learning based ones) qualitatively and quantitatively, especially in the scenario of limited training samples. I. INTRODUCTION With the rapid development of hyperspectral imaging techniques, current sensors always have high spectral and spatial resolution [1]. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61502195), and in part by the Office of Naval Research Science of Autonomy (Grant No. N000141812306). Yantao Wei is with School of Educational Information Technology, Central China Normal University, Wuhan 430079, China (email: yantaowei@mail.ccnu.edu.cn).

Partially Observable Markov Decision Processes (POMDPs) are a popular formalism for sequential decision making in partially observable environments. Since solving POMDPs to optimality is a difficult task, point-based value iteration methods are widely used. These methods compute an approximate POMDP solution, and in some cases they even provide guarantees on the solution quality, but these algorithms have been designed for problems with an infinite planning horizon. In this paper we discuss why state-of-the-art point-based algorithms cannot be easily applied to finite-horizon problems that do not include discounting. Subsequently, we present a general point-based value iteration algorithm for finite-horizon problems which provides solutions with guarantees on solution quality. Furthermore, we introduce two heuristics to reduce the number of belief points considered during execution, which lowers the computational requirements. In experiments we demonstrate that the algorithm is an effective method for solving finite-horizon POMDPs.