A novel framework of the fuzzy c-means distances problem based weighted distance Andy Arief Setyawan a,1,, Ahmad Ilham b,1 a Department of Information and Communication, Pemalang District Government, Pemalang, Indonesia b Department of Informatics, Universitas Muhammadiyah Semarang, Semarang 50354, Indonesia Abstract Clustering is one of the major roles in data mining that is widely application in pattern recognition and image segmentation. Fuzzy C-means (FCM) is the most used clustering algorithm that proven efficient, fast and easy to implement, however FCM uses the Euclidean distance that often leads to clustering errors, especially when handling multidimensional and noisy data. In the last few years, many distances metric have been propose by researchers to improve the performance of the FCM algorithms, and the majority of researchers propose weighted distance. In this paper, we proposed Canberra Weighted Distance to improved performance of the FCM algorithm. Experimental result using the UCI data set show the proposed method is superior to the original method and other clustering methods. Keywords: clustering, fuzzy c-means, euclidean distance, weighted distance, canberra distance 1. Introduction Cluster analysis or clustering is the process of partitioning a set of data objects into subset or clusters, where the objects in a cluster is similar to onenull This document is a collaborative effort by Intelligent Systems Research Group Indonesia and Informatics Department Universitas Muhammadiyah Semarang.

LONDON - In decades to come, African farmers may pool their money to buy small robot vehicles to weed their fields or drones that can hover to squirt a few drops of pesticide only where needed. Smartphones already allow farmers in remote areas to snap photos of sick plants, upload them and get a quick diagnosis, plus advice on treatment. Researchers also are trying to train crops like maize and wheat to produce their own nitrogen fertilizer from the air -- a trick soybeans and other legumes use -- and exploring how to make wheat and rice better at photosynthesis in very hot conditions. As warmer, wilder weather linked to climate change brings growing challenges for farmers across the globe -- and as they try to curb their own heat-trapping emissions -- a rush of innovation aimed at helping both rich and poor farmers is now converging in ways that could benefit them all, scientists say. In a hotter world, farmers share "the same problems, the same issues," said Svend Christensen, head of plant and environmental sciences at the University of Copenhagen.

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than two million aircraft. We show that we can obtain the correct aircraft category with an accuracy of over 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect sensitive aircraft such as police and surveillance aircraft using this method.

These and many other insights are from Capgemini's Reinventing Cybersecurity with Artificial Intelligence Report published this week. Capgemini Research Institute surveyed 850 senior executives from seven industries, including consumer products, retail, banking, insurance, automotive, utilities, and telecom. Enterprises headquartered in France, Germany, the UK, the US, Australia, the Netherlands, India, Italy, Spain, and Sweden are included in the report. Please see page 21 of the report for a description of the methodology. Capgemini found that as digital businesses grow, their risk of cyberattacks exponentially increases.

Three New Jersey teens brought home two international awards for their artificial intelligence robot, who competed at the International Robocup Junior Championship in Sydney, Australia earlier this month. The team -- made up of high school juniors Julian Lee of Livingston and Jeffrey Cheng from Bridgewater, and senior Alexander Lisenko, also of Bridgewater -- won the third place World Title for Individual Team Tournament, and the Judge's Award for Best Rescue Engineering Strategy in the Rescue Maze League. The trio belongs to Storming Robots, a New Jersey-based Robotics Learning Lab, and competed against teams of 14- to 19-year-olds from around the world in the July 4-9 contest. "The competition went by quick despite the many hours of work. It was an exciting but stressful experience, which was especially fun due to our great team dynamic," Lee said.

The South Australian Government had recently launched a free autonomous vehicle trial that will deliver a first and last mile service between the Playford Alive Township and the Munno Para Train Station. As reported, the autonomous electric bus will run approximately every half hour on weekdays and will carry passengers from the Munno Para Railway Station to the Playford Alive Township, including stops at Mark Oliphant College and the Stretton Centre. The free autonomous shuttle bus service will include a morning and afternoon service window, with passengers collected from designated pick up points on the route. The Munno Para service is phase two of the Playford Connect autonomous bus trial. It follows phase one, which provided a free park and ride service from the Lionsgate Carpark through to the Lyell McEwin Hospital in Elizabeth Vale from October 2018 to June 2019.

How can we trust the networks?" They answered: "We know that a network is quite reliable when its inputs come from its training set. But these critical systems will have inputs corresponding to new, often unanticipated situations. There are numerous examples where a network gives poor responses for untrained inputs." David Lorge Parnas followed up on this discussion in his Letter to the Editor (Feb. We wish to point out that machine learning-based systems, including commercial ones performing safety critical tasks, can fail not only under "unanticipated situations" (noted by Lewis and Denning) or "when it encounters data radically different from its training set" (noted by Parnas), but also under normal situations, even on data that is extremely similar to its training set. The Apollo self-driving team confirmed "it might happen" because the system was "deep learning trained." Now, after a further investigation, we have found that in 24 of these 27 failed tests, the 10 random points ...

Bayesian synthetic likelihood (BSL) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a computationally intractable likelihood function. Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared to alternative methods such as approximate Bayesian computation (ABC), BSL requires little tuning and requires less model simulations than ABC when the chosen summary statistic is high-dimensional. The original synthetic likelihood relies on a multivariate normal approximation of the intractable likelihood, where the mean and covariance are estimated by simulation. An extension of BSL considers replacing the sample covariance with a penalised covariance estimator to reduce the number of required model simulations. Further, a semi-parametric approach has been developed to relax the normality assumption. In this paper, we present an R package called BSL that amalgamates the aforementioned methods and more into a single, easy-to-use and coherent piece of software. The R package also includes several examples to illustrate how to use the package and demonstrate the utility of the methods.

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.

Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective. We will study the spectra of the *Conjugate Kernel*, CK, (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel*, NTK. Roughly, the CK and the NTK tell us respectively "what a network looks like at initialization"and "what a network looks like during and after training." Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks. By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks. We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks. We have open-sourced the code for it and for generating the plots in this paper at github.com/thegregyang/NNspectra.