Botnets are one of the online threats with the biggest presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze large amounts of network traffic data. In this work, we focus on increasing the performance on botnet traffic classification by selecting those features that further increase the detection rate. For this purpose we use two feature selection techniques, Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features. Then, we evaluate the three feature subsets along with three models, Decision Tree, Random Forest and k-Nearest Neighbors. To test the performance of the three feature vectors and the three models we generate two datasets based on the CTU-13 dataset, namely QB-CTU13 and EQB-CTU13. We measure the performance as the macro averaged F1 score over the computational time required to classify a sample. The results show that the highest performance is achieved by Decision Trees using a five feature set which obtained a mean F1 score of 85% classifying each sample in an average time of 0.78 microseconds.

We cast aspects of consciousness in axiomatic mathematical terms, using the graphical calculus of general process theories (a.k.a symmetric monoidal categories and Frobenius algebras therein). This calculus exploits the ontological neutrality of process theories. A toy example using the axiomatic calculus is given to show the power of this approach, recovering other aspects of conscious experience, such as external and internal subjective distinction, privacy or unreadability of personal subjective experience, and phenomenal unity, one of the main issues for scientific studies of consciousness. In fact, these features naturally arise from the compositional nature of axiomatic calculus.

Machine learning (ML) has the power to take an organization's digital transformation to dizzying new heights. While this is a well known fact amongst business leaders in the enterprise, full scale ML implementation is often perceived as unattainable. This, however, could not be further from the truth. For those that are open to new ways of thinking, the endless possibilities created by even minor ML deployments - such as decreased costs and helping teams to work more efficiently - are up for grabs. And many business leaders are seizing the opportunity to integrate it into their current IT infrastructure.

The continuous progress of technology has led to different government organizations having to modify their structures, as well as the way in which they execute their processes. Nowadays, applying tools such as Artificial Intelligence (AI) in government is essential, since AI makes all operations more efficient, allows citizens to listen better, have greater sensitivity about what they are asking for, what they need, and know the general feeling you have. In other words, it can be said that Artificial Intelligence is an extraordinary content source for the public sector and, above all, it is a great value . Many developed and developing countries are already implementing AI in different activities within the Public Administration. An example of this is what the Government of Finland is doing, which is conducting tests with what is considered, so far, the most ambitious public assistant based on Artificial Intelligence in the world: AuroraAI .

The artificial intelligence (AI) chips market is expected to grow by USD 73.49 billion at over 51% CAGR during 2021-2025, according to the latest market research report by Technavio. The report provides a detailed analysis of the market by analyzing the impact of the COVID-19 pandemic on businesses. According to Technavio, the COVID-19 pandemic will have a positive impact on the growth of the artificial intelligence (AI) chips market. The report expects the market value to increase in 2021 as compared to 2020. Many businesses currently are going through response, recovery, and renewal phases.

We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse, and furthermore can answer multiple counterfactual questions in parallel while learning from previous queries. We formulate CFE search for regression models in a rigorous mathematical framework using differentiable potentials, which resolves robustness issues in threshold-based objectives. We prove that in this framework, (a) verifying the existence of counterfactuals is NP-complete; and (b) that finding instances using such potentials is CLS-complete. We describe a unified algorithm for CFEs using a specialised acquisition function that composes both expected improvement and an exponential-polynomial (EP) family with desirable properties. Our evaluation on real-world benchmark domains demonstrate high sample-efficiency and precision.

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst.

Editor's note: This year What If?, our annual collection of scenarios, considers the future of health. Each of these stories is fiction, but grounded in historical fact, current speculation and real science. IT WAS A scene that the Nobel committee had dearly hoped to avoid. As the recipients of this year's prizes filed into the Stockholm Concert Hall to take their seats, dozens of protesters, including several former laureates, clashed with police in the streets outside. They had gathered to express their opposition to the unprecedented decision to award the Nobel prize in physiology or medicine to an artificial intelligence.

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as: "the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it may take the form: "the more X, the more Y, almost 3 months later". In our third contribution, we propose a data crossing model that allows for integration of mostly gradual pattern mining algorithm implementations into a Cloud platform. This contribution is motivated by the proliferation of IoT applications in almost every area of our society and this comes with provision of large-scale time-series data from different sources.

Global Artificial Intelligence in Medical Imaging Market report identifies key players operating in the market and comprehensively analyzes their market rankings and core competencies. The report describes major factors (drivers, restraints, opportunities, and challenges) influencing the growth of the market and submarkets. It enhances the decision-making process by understanding the strategies that underpins commercial interest with respect to products, segmentation, and industry verticals. This marketing report helps develop/modify business expansion plans by using substantial growth offering developed and emerging markets. The worldwide report gives strategic recommendations in key business sections in light of the market estimations.