In this study, we predict the outcome of the football matches in the FIFA World Cup 2018 to be held in Russia this summer. We do this using classification models over a dataset of historic football results that includes attributes from the playing teams by rating them in attack, midfield, defence, aggression, pressure, chance creation and building ability. This last training data was a result of merging international matches results with AE games ratings of the teams considering the timeline of the matches with their respective statistics. Final predictions show the four countries with the most chances of getting to the semifinals as France, Brazil, Spain and Germany while giving Spain as the winner. The objective of this study is to build a predictive model that will allow us to make good predictions for the coming World Cup 2018 so we looked for dataset with historic data for match results, for this purpose we chose a dataset from Kaggle with data of almost 40,000 international matches played between 1872 and 2018.

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning. However, reinforcement learning agents have only recently been endowed with such capacity for hindsight. In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms. Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.

We consider fractional hedonic games, a subclass of coalition formation games that can be succinctly modeled by means of a graph in which nodes represent agents and edge weights the degree of preference of the corresponding endpoints. The happiness or utility of an agent for being in a coalition is the average value she ascribes to its members. We adopt Nash stable outcomes as the target solution concept; that is we focus on states in which no agent can improve her utility by unilaterally changing her own group. We provide existence, efficiency and complexity results for games played on both general and specific graph topologies. As to the efficiency results, we mainly study the quality of the best Nash stable outcome and refer to the ratio between the social welfare of an optimal coalition structure and the one of such an equilibrium as to the price of stability. In this respect, we remark that a best Nash stable outcome has a natural meaning of stability, since it is the optimal solution among the ones which can be accepted by selfish agents. We provide upper and lower bounds on the price of stability for different topologies, both in case of weighted and unweighted edges. Beside the results for general graphs, we give refined bounds for various specific cases, such as triangle-free, bipartite graphs and tree graphs. For these families, we also show how to efficiently compute Nash stable outcomes with provable good social welfare.

Every time the World Cup rolls around, there is always a random animal that can mysteriously predict the entire tournament. Previously we've seen the likes of Paul the Octopus, the physic turtle and this year we have'Mystic Marcus' the micro pig. Should we really be trusting an animal with our World Cup betting, though? Surely we should instead put all our faith in the things that now control our lives: technology. Technische Universitat Dortmund, Ghent University and the Technical University of Munich have developed an AI system that has analysed 100,000 simulations for this summer's tournament in Russia.

In the previous blog posts in this series, we discussed the motivation for clustering attacks and the data used and how to calculate the distance between two attacks using different methods on each feature we extracted. In this final blog post, we'll discuss the clustering algorithm itself – how to use the distance we calculated to create clusters from the data. We will discuss clustering in real time when only a small amount of data can be stored in memory. Finally, we'll show some results of the algorithm based on real data from Imperva customers. Now we have all the basic ingredients to input into the algorithm.

Washington: Scientists have developed an artificial intelligence system that can accurately diagnose Zika virus and several other viral, bacterial and even genetic diseases from the patient's blood. The platform developed by scientists at the University of Campinas (UNICAMP) in Brazil, can identify tens of thousands of molecules present in blood serum, with an artificial intelligence algorithm. "We used infection by Zika virus as a model to develop the platform and showed that in this case, diagnostic accuracy exceeded 95%. One of the main advantages is that the method doesn't lose sensitivity even if the virus mutates," said Rodrigo Ramos Catharino, principal investigator at UNICAMP. Another strength of the platform, he added, is the capacity to identify positive cases of Zika even in blood serum analysed 30 days after the start of infection, when the acute phase of the disease is over.

Genetic Algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposes in this paper is able to work with more variable from some adjustments on hardware architecture.

Deep Learning methods are currently the state-of-the-art in many problems which can be tackled via machine learning, in particular classification problems. However there is still lack of understanding on how those methods work, why they work and what are the limitations involved in using them. In this chapter we will describe in detail the transition from shallow to deep networks, include examples of code on how to implement them, as well as the main issues one faces when training a deep network. Afterwards, we introduce some theoretical background behind the use of deep models, and discuss their limitations. Training restricted boltzmann machines: An introduction.

Ensuring the security of transactions is currently one of the major challenges facing banking systems. The usage of face for biometric authentication of users is becoming adopted worldwide due its convenience and acceptability by people, and also given that, nowadays, almost all computers and mobile devices have built-in cameras. Such user authentication approach is attracting large investments from banking and financial institutions, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) taken with the cameras of mobile devices, for the automated opening of new checking accounts or financial transactions authorization. In this work, besides of collecting a large cross-domain face database, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the systems of the major public Brazilian bank, we propose a novel approach for such cross-domain face matching based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the large dataset collected, which we called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain problem (comparing faces in IDs and selfies) and its feasible application in real banking security systems.

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.