The market research report on the global USA Machine Learning in Communication industry provides a comprehensive study of the various techniques and materials used in the production of USA Machine Learning in Communication market products. Starting from industry chain analysis to cost structure analysis, the report analyzes multiple aspects, including the production and end-use segments of the USA Machine Learning in Communication market products. The latest trends in the pharmaceutical industry have been detailed in the report to measure their impact on the production of USA Machine Learning in Communication market products. This report comes along with an added Excel data-sheet suite taking quantitative data from all numeric forecasts presented in the report. Research Methodology: The USA Machine Learning in Communication market has been analyzed using an optimum mix of secondary sources and benchmark methodology besides a unique blend of primary insights.

Hector Geffner got his Ph.D at UCLA with a dissertation that was co-winner of the 1990 ACM Dissertation Award. He then worked as Staff Research Member at the IBM T.J. Watson Research Center in NY, USA and at the Universidad Simon Bolivar, in Caracas, Venezuela. Since 2001, he is a researcher at ICREA and a professor at the Universitat Pompeu Fabra, Barcelona. He is a former Associate Editor of Artificial Intelligence and the Journal of Artificial Intelligence Research. He is also a member of the EurAI board, a Fellow of AAAI and EurAI, and author of the book Default Reasoning: Causal and Conditional Theories'', MIT Press, 1992, editor of "Heuristics, Probability, and Causality: a Tribute to Judea Pearl" along with R. Dechter and Joe Halpern, College Publications, 2010, and author with Blai Bonet of "A Concise Introduction to Models and Methods for Automated Planning", Morgan and Claypool, 2013.

A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation technique with multiple deep learning techniques, which construct models are capable of generating execution traces with timestamped events. The study sheds light into the relative strengths of both approaches and raises the prospect of developing hybrid approaches that combine these strengths.

In several research problems we face probabilistic sequences of inputs (e.g., sequence of stimuli) from which an agent generates a corresponding sequence of responses and it is of interest to model/discover some kind of relation between them. To model such relation in the context of statistical learning in neuroscience, a new class of stochastic process have been introduced [5], namely sequences of random objects driven by context tree models. In this paper we introduce a freely available Matlab toolbox (SeqROCTM) that implements three model selection methods to make inference about the parameters of this kind of stochastic process.

One of the main issue in robotics is the lack of embedded computational power. Recently, state of the art algorithms providing a better understanding of the surroundings (Object detection, skeleton tracking, etc.) are requiring more and more computational power. The lack of embedded computational power is more significant in mass-produced robots because of the difficulties to follow the increasing computational requirements of state of the art algorithms. The integration of an additional GPU allows to overcome this lack of embedded computational power. We introduce in this paper a prototype of Pepper with an embedded GPU, but also with an additional 3D camera on the head of the robot and plugged to the late GPU. This prototype, called Adapted Pepper, was built for the European project called MuMMER (MultiModal Mall Entertainment Robot) in order to embed algorithms like OpenPose, YOLO or to process sensors information and, in all cases, avoid network dependency for deported computation.

A few Recommender Systems (RS) resort to explanations so as to enhance trust in recommendations. However, current techniques for explanation generation tend to strongly uphold the recommended products instead of presenting both reasons for and reasons against them. We argue that an RS can better enhance overall trust and transparency by frankly displaying both kinds of reasons to users.We have developed such an RS by exploiting knowledge graphs and by applying Snedegar's theory of practical reasoning. We show that our implemented RS has excellent performance and we report on an experiment with human subjects that shows the value of presenting both reasons for and against, with significant improvements in trust, engagement, and persuasion.

Detecting changed regions in paired satellite images plays a key role in many remote sensing applications. The evolution of recent techniques could provide satellite images with very high spatial resolution (VHR) and made it challenging to apply image coregistration whose accuracy is the basis of many change detection methods.Due to the advantage in deep feature representation, deep learning is introduced to detect changes on unregistered images. However, the absence of ground truth makes the performance of deep learning models in unsupervised task hard to be evaluated or be guaranteed.To alleviate the effect of unregistered pairs and make better use of deep learning structures, we propose a novel change detection procedure based on a special neural network architecture---Generative Adversarial Network (GAN).GAN features generating realistic images rather than giving hypervectors that contain visual features, so it is easy to evaluate the GAN model by judging the generated images. In this paper, we show that GAN model can be trained upon a pair of images through utilizing the proposed expanding strategy to create a training set and optimising designed objective functions. The optimised GAN model would produce many coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy using these generated images explicitly.Compared to other deep learning-based methods, our method is less sensitive to the problem of unregistered images and makes most of the deep learning structure.Experimental results on synthetic images and real data with many different scenes could demonstrate the effectiveness of the proposed approach.

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC.

Human race suffers from the God Complex. Art and science strive to achieve recreate the human form, thought pattern, aesthetics, and ethics. Can we replicate the human intellect by making machines think for themselves? Artificial intelligence does not face the moral dilemma of making choices that fall in the grey area, it is binary in its output. The concept of GIGO – garbage in, garbage out holds in the case of AI too.