Given the large variety of existing logical formalisms it is of utmost importance to select the most adequate one for a specific purpose, e.g. for representing the knowledge relevant for a particular application or for using the formalism as a modeling tool for problem solving. Awareness of the nature of a logical formalism, in other words, of its fundamental intrinsic properties, is indispensable and provides the basis of an informed choice. One such intrinsic property of logic-based knowledge representation languages is the context-dependency of pieces of knowledge. In classical propositional logic, for example, there is no such context-dependence: whenever two sets of formulas are equivalent in the sense of having the same models (ordinary equivalence), then they are mutually replaceable in arbitrary contexts (strong equivalence). However, a large number of commonly used formalisms are not like classical logic which leads to a series of interesting developments.

Applied TPB and UTAUT to assess a priori acceptance of highly automated cars. Drivers residing in France reported greater intentions to use highly automated cars in the future. More research is required to further assess the feasibility of the TPB and UTAUT to assess intentions to use AVs. To assess and explain finely drivers’ a priori acceptance of highly automated cars, this study used the Theory of Planned Behaviour (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Further, the current study sought to extend upon previous research to assess if intentions to use highly automated cars in the future differed according to country (i.e., Australia, France, & Sweden).

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.

We study the forecasting of the power consumptions of a population of households and of subpopulations thereof. These subpopulations are built according to location, to exogenous information and/or to profiles we determined from historical households consumption time series. Thus, we aim to forecast the electricity consumption time series at several levels of households aggregation. These time series are linked through some summation constraints which induce a hierarchy. Our approach consists in three steps: feature generation, aggregation and projection. Firstly (feature generation step), we build, for each considering group for households, a benchmark forecast (called features), using random forests or generalized additive models. Secondly (aggregation step), aggregation algorithms, run in parallel, aggregate these forecasts and provide new predictions. Finally (projection step), we use the summation constraints induced by the time series underlying hierarchy to re-conciliate the forecasts by projecting them in a well-chosen linear subspace. We provide some theoretical guaranties on the average prediction error of this methodology, through the minimization of a quantity called regret. We also test our approach on households power consumption data collected in Great Britain by multiple energy providers in the Energy Demand Research Project context. We build and compare various population segmentations for the evaluation of our approach performance.

We prove that every concept class with finite Littlestone dimension can be learned by an (approximate) differentially-private algorithm. This answers an open question of Alon et al. (STOC 2019) who proved the converse statement (this question was also asked by Neel et al.~(FOCS 2019)). Together these two results yield an equivalence between online learnability and private PAC learnability. We introduce a new notion of algorithmic stability called "global stability" which is essential to our proof and may be of independent interest. We also discuss an application of our results to boosting the privacy and accuracy parameters of differentially-private learners.

Recent drought and population growth are planting unprecedented demand for the use of available limited water resources. Irrigated agriculture is one of the major consumers of freshwater. A large amount of water in irrigated agriculture is wasted due to poor water management practices. To improve water management in irrigated areas, models for estimation of future water requirements are needed. Developing a model for forecasting irrigation water demand can improve water management practices and maximise water productivity. Data mining can be used effectively to build such models. In this study, we prepare a dataset containing information on suitable attributes for forecasting irrigation water demand. The data is obtained from three different sources namely meteorological data, remote sensing images and water delivery statements. In order to make the prepared dataset useful for demand forecasting and pattern extraction, we pre-process the dataset using a novel approach based on a combination of irrigation and data mining knowledge. We then apply and compare the effectiveness of different data mining methods namely decision tree (DT), artificial neural networks (ANNs), systematically developed forest (SysFor) for multiple trees, support vector machine (SVM), logistic regression, and the traditional Evapotranspiration (ETc) methods and evaluate the performance of these models to predict irrigation water demand. Our experimental results indicate the usefulness of data pre-processing and the effectiveness of different classifiers. Among the six methods we used, SysFor produces the best prediction with 97.5% accuracy followed by a decision tree with 96% and ANN with 95% respectively by closely matching the predictions with actual water usage. Therefore, we recommend using SysFor and DT models for irrigation water demand forecasting.

They are a fundamental datatype in all the modern programming languages, and operations on strings frequently occur in disparate fields such as software analysis, model checking, database applications, web security, bioinformatics and so on[3, 11, 19, 21, 27, 28, 49, 60, 67]. Reasoning over strings requires solving arbitrarily complex string constraints, i.e., relations defined on a number of string variables. Typical examples of string constraints are string length, (dis-)equality, concatenation, substring, regular expression matching. With the term "string constraint solving" (in short, string solving or SCS) we refer to the process of modelling, processing, and solving combinatorial problems involving string constraints. We may see SCS as a declarative paradigm which falls at the intersection between constraint solving and combinatorics on words: the user states a problem with string variables and constraints, and a suitable string solver seeks a solution for that problem. Although works on the combinatorics of words were already published in the 1940s [110], the dawn of SCS dates back to the late 1980s in correspondence with the rise of Constraint Programming (CP) [114] and Constraint Logic Programming(CLP)[73] paradigms. Pioneers in this field were for example Trilogy[142], a language providing strings, integer and real constraints, and CLP(Σ) [144], an instance of the CLP scheme representing strings as regular sets. The latter in particular was the first known attempt to use string constraints like regular membership to denote regular sets.

"What would a candle inspired by your book smell like?" This was a question that was going around the other day, and most authors got to answer with something nice. My book, however… let's just say it features a neural net-generated recipe called "Basic Clam Frosting". But what kind of candles would a neural net invent? Training a neural net to generate candles was a good (and rather frightening) suggestion.

The major factors propelling the growth of the Global Artificial Intelligence (AI) market include the rise in the adoption of cloud-based applications and services and the growth of big data across the globe. The advanced AI technologies is continuously enhancing the performance of economies, businesses and different industries. The growing demand for intelligent virtual assistants across different verticals in several industries will have a positive impact on the Global Artificial Intelligence (AI) market during the forecast period. The increased integration of image recognition technology with optical character recognition, pattern matching and face recognition in various end-use applications such as drones, self-driving cars and robotics will propel the growth of the artificial intelligence market during the forecast period. Various multinational giants are largely focusing on mergers and acquisitions with emerging start-ups in order to capture the highest market share and gain a competitive advantage over the other market players.

For instance, a mixture of primary and secondary research has been used to define Artificial Intelligence Software market estimates and forecasts. Sources used for secondary research contain (but not limited to) Paid Data Sources, Technology Journals of 2013-2018, SEC Filings Company Websites, Annual Reports, and various other Artificial Intelligence Software industry publications. Specific details on the methodology used for Artificial Intelligence Software market report can be provided on demand. In addition, It highlights the ability to increase possibilities in the coming years by 2023, also reviewing the marketplace drivers, constraints and restraints, growth signs, challenges, market dynamics. "Global Artificial Intelligence Software Market" gives a region-wise analysis like growth aspects, and revenue, Past, present and future forecast trends, Analysis of emerging market sectors and development opportunities in Artificial Intelligence Software will forecast the market growth. Regional scope: Artificial Intelligence Software market is divided into various regions like North America, Middle-East a and Africa, Asia-Pacific, South America, and Europe. Country scope: Artificial Intelligence Software market is divided into United States, Mexico, Canada, Germany, Singapore, U.K., Italy, Russia, France, Spain, China, India, Japan, South Korea, Australia, Brazil, Colombia, Paraguay, Saudi Arabia, South Africa, Egypt, and UAE, ASEAN countries.