Simulate Keyboard Press and Mouse Click (KeyboardSimulator)Control your keyboard and mouse with R code, simulate key press and mouse click.

Multivariate Renewal Hawkes Process (MRHawkes)Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting process, with given immigrant hazard rate functions and offspring density f …

Stacked Area Plot Stacked Area Graphs work in the same way as simple Area Graphs do, except for the use of multiple data series that start each point from the point left by the previous data series. The entire graph represents the total of all the data plotted. Stacked Area Graphs also use area to convey whole numbers, so they do not work for negative values. Overall, they are useful for comparing multiple variables changing over an interval. …

Irregular Convolutional Neural Network (ICNN) Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN). Compared to traditional CNN applying regular convolutional kernels like ${3\times3}$, our approach trains irregular kernel shapes to better fit the geometric variations of input features. In other words, shapes are learnable parameters in addition to weights. The kernel shapes and weights are learned simultaneously during end-to-end training with the standard back-propagation algorithm. Experiments for semantic segmentation are implemented to validate the effectiveness of our proposed ICNN. …

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area. A Tutorial on Network Embeddings

• Crunchbase lists over 5,000 startups who are relying on machine learning for their main and ancillary applications, products and services today.

I was reading this overview of mixed-effect modeling in ecology, and thought you or your blog readers may be interested in their last conclusion (page 35):

Conceptual formalism supported by typical ontologies may not be sufficient to represent uncertainty information which is caused due to the lack of clear cut boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer a way to deal with this uncertainty. This paper describes the state of the art in developing fuzzy ontologies. The survey is produced by studying about 35 works on developing fuzzy ontologies from a batch of 100 articles in the field of fuzzy ontologies. The State of the Art in Developing Fuzzy Ontologies: A Survey

Model, MetaModel and Anomaly Detection (M3A) Alice’ is submitting one web search per five minutes, for three hours in a row – is it normal? How to detect abnormal search behaviors, among Alice and other users? Is there any distinct pattern in Alice’s (or other users’) search behavior? We studied what is probably the largest, publicly available, query log that contains more than 30 million queries from 0.6 million users. In this paper, we present a novel, user-and group-level framework, M3A: Model, MetaModel and Anomaly detection. For each user, we discover and explain a surprising, bi-modal pattern of the inter-arrival time (IAT) of landed queries (queries with user click-through). Specifically, the model Camel-Log is proposed to describe such an IAT distribution; we then notice the correlations among its parameters at the group level. Thus, we further propose the metamodel Meta-Click, to capture and explain the two-dimensional, heavy-tail distribution of the parameters. Combining Camel-Log and Meta-Click, the proposed M3A has the following strong points: (1) the accurate modeling of marginal IAT distribution, (2) quantitative interpretations, and (3) anomaly detection. …