Hierarchical computational methods for multiscale mechanics such as the FE$^2$ and FE-FFT methods are generally accompanied by high computational costs. Data-driven approaches are able to speed the process up significantly by enabling to incorporate the effective micromechanical response in macroscale simulations without the need of performing additional computations at each Gauss point explicitly. Traditionally artificial neural networks (ANNs) have been the surrogate modeling technique of choice in the solid mechanics community. However they suffer from severe drawbacks due to their parametric nature and suboptimal training and inference properties for the investigated datasets in a three dimensional setting. These problems can be avoided using local approximate Gaussian process regression (laGPR). This method can allow the prediction of stress outputs at particular strain space locations by training local regression models based on Gaussian processes, using only a subset of the data for each local model, offering better and more reliable accuracy than ANNs. A modified Newton-Raphson approach is proposed to accommodate for the local nature of the laGPR approximation when solving the global structural problem in a FE setting. Hence, the presented work offers a complete and general framework enabling multiscale calculations combining a data-driven constitutive prediction using laGPR, and macroscopic calculations using an FE scheme that we test for finite-strain three-dimensional hyperelastic problems.

Advertising click fraud detection plays one of the vital roles in current E-commerce websites as advertising is an essential component of its business model. It aims at, given a set of corresponding features, e.g., demographic information of users and statistical features of clicks, predicting whether a click is fraudulent or not in the community. Recent efforts attempted to incorporate attributed behavior sequence and heterogeneous network for extracting complex features of users and achieved significant effects on click fraud detection. In this paper, we propose a Multimodal and Contrastive learning network for Click Fraud detection (MCCF). Specifically, motivated by the observations on differences of demographic information, behavior sequences and media relationship between fraudsters and genuine users on E-commerce platform, MCCF jointly utilizes wide and deep features, behavior sequence and heterogeneous network to distill click representations. Moreover, these three modules are integrated by contrastive learning and collaboratively contribute to the final predictions. With the real-world datasets containing 2.54 million clicks on Alibaba platform, we investigate the effectiveness of MCCF. The experimental results show that the proposed approach is able to improve AUC by 7.2% and F1-score by 15.6%, compared with the state-of-the-art methods.

Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting soft information (information that cannot be quantified) and making predictions in unknowable risk situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the gold standard for assessing soft signals and make use of intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.

Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed in different organizations, i.e., the curse of isolated data islands. To address this problem, we incorporate federated learning into GL and propose a general Federated Graph Learning framework FedGL, which is capable of obtaining a high-quality global graph model while protecting data privacy by discovering the global self-supervision information during the federated training. Concretely, we propose to upload the prediction results and node embeddings to the server for discovering the global pseudo label and global pseudo graph, which are distributed to each client to enrich the training labels and complement the graph structure respectively, thereby improving the quality of each local model. Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients. Finally, experimental results show that FedGL significantly outperforms baselines on four widely used graph datasets.

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.

In the last tutorial, we completed the Data Pre-Processing step. We saw preprocessing techniques applied in transformation and variable selection, dimensionality reduction, and sampling for machine learning throughout this previous tutorial. Now we can move on to the next steps within the Data Science process, where we'll apply the rest of the model building process with various classification algorithms to understand what it is and how to use machine learning with python language. In the next moment, we will discuss the Regression algorithms. We will not go into detail about the algorithms. The purpose here will be to understand the detailed process of building the Machine Learning model, machine learning, model evaluation, and prediction scans. See The Jupyter Notebook for the concepts we'll cover on building machine learning models and my LinkedIn profile for other Data Science articles and tutorials. The metrics chosen to evaluate model performance will influence how performance is measured and compared to models created with other algorithms. We need to find a metric to measure performance between models solidly and coherently, a metric comparable to the models analyzed. Let's use the same algorithm, but with different metrics, and so compare the results.

This developer code pattern uses Findability Platform (FP) Predict Plus operator from Red Hat Marketplace to predict customer spending using historical data and demonstrates the automated process of building models. Machine learning is a large field of study that overlaps with and inherits ideas from many related fields, such as artificial intelligence. The focus of the field is learning -- that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data. As such, there are many types of learning you may encounter as a practitioner in the field of machine learning from whole fields of study to specific techniques.

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

There is a range of applications where the interest is in understanding interactions between a set of time series, including in neuroscience, genomics, econometrics, climate science, and social media analysis. For example, in neuroscience, one may seek to understand whether activity in one brain region correlates with later activity in another region, or to decipher instantaneous correlations between regions--both notions of functional connectivity. In genomics, there is an analogous study of gene regulatory networks. In econometrics, one may be interested in how various macroeconomic indicators predict one another. We also have unprecedented levels of data on people's actions--whether they be social media posts, purchase histories, or political voting records--and want to understand the dependencies between the actions of these individuals. Modern recording modalities and the ability to store and process large amounts of data have escalated the scale at which we seek to do such analyses. In many cases, one may seek notions of causal interactions amongst the time series, but be limited to drawing inferences from observational data without opportunities for experimentation and without known mechanistic models for the observed phenomena.

K-Means++ and its distributed variant K-Means$\|$ have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation, and theoretical grounding of the K-means++ and $\|$ methods have made them difficult to "best" from a holistic perspective. By considering the limited opportunities within seed selection to perform pruning, we develop specialized triangle inequality pruning strategies and a dynamic priority queue to show the first acceleration of K-Means++ and K-Means$\|$ that is faster in run-time while being algorithmicly equivalent. For both algorithms we are able to reduce distance computations by over $500\times$. For K-means++ this results in up to a 17$\times$ speedup in run-time and a $551\times$ speedup for K-means$\|$. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.