--Recreating cyber-attack alert data with a high level of fidelity is challenging due to the intricate interaction between features, non-homogeneity of alerts, and potential for rare yet critical samples. Generative Adversarial Networks (GANs) have been shown to effectively learn complex data distributions with the intent of creating increasingly realistic data. This paper presents the application of GANs to cyber-attack alert data and shows that GANs not only successfully learn to generate realistic alerts, but also reveal feature dependencies within alerts. This is accomplished by reviewing the intersection of histograms for varying alert-feature combinations between the ground truth and generated datsets. Traditional statistical metrics, such as conditional and joint entropy, are also employed to verify the accuracy of these dependencies. Finally, it is shown that a Mutual Information constraint on the network can be used to increase the generation of low probability, critical, alert values. By mapping alerts to a set of attack stages it is shown that the output of these low probability alerts has a direct contextual meaning for Cyber Security analysts. Overall, this work provides the basis for generating new cyber intrusion alerts and provides evidence that synthesized alerts emulate critical dependencies from the source dataset. I NTRODUCTION Classifying, predicting, and generating cyber-attack alert data provides a unique set of challenges due to imbalance and a lack of homogeneity in alert datasets. Furthering these challenges critical exploits in a network are often rare and difficult to identify. Despite this is has been shown that alert data can be used to identify anomalous traffic [1] [2] [3], network vulnerabilities [4], and bad actor behavior profiling [5]. However, to fully realize the potential of cyber-attack alert data, a means to acquire more data and analyze critical dependencies within alerts is needed. This work seeks to provide solutions to these challenges by showing that deep learning models are able to recreate cyber-attack alert data when given representative real world data. This includes a means for driving better coverage of the feature domain in model outputs, allowing more rare but critical events to be synthesized.

A new study suggest that the same racial stereotypes applied to people are also applied to their mechanical kin. Researchers from the Human Interface Technology Laboratory in New Zealand say humans perceive robots that resemble humans to have a certain race and may apply stereotypes on the bot depending on the shade of its'skin'. The findings come from what's known as a shooter bias test. In the experiment, participants were shown various images of armed and unarmed subjects and asked to make a split-second reaction test based on the level of'threat.' Robots are more likely to be deemed a threat if their'skin' is darker An affirmative reaction came in the form of participants pressing a button, or in other words, choosing to pull the trigger. What they found was that people were more apt to'shoot' robots with darker tones than lighter ones even when they were posing no threat.

My family and I continue to have more and more conversations with Alexa, Siri and Google Assistant lately. Having three AI based sources within speaking range of each other, we have a tendency to fact check them against one another - especially when someone doesn't quite trust or agree with the answer they get. For example, is Australia considered a continent or is it Oceania? Is a hot dog a sandwich? Who is the best NBA player of all time ever?

--Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix F actorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present in different items. In the model, predicted item ratings are regarded as weighted aggregations of attribute ratings generated by the inner product of the user latent vectors and the attribute latent vectors. MMF provides more fine grained analyses than matrix factorization in the following ways: attribute ratings with weights allow the understanding of how much each attribute contributes to the recommendation and hence provide interpretability; the common attributes can act as a link between existing and new items, which solves the item cold-start problem when no rating exists on an item. We evaluate the interpretability of MMF comprehensively, and conduct extensive experiments on real datasets to show that MMF outperforms state-of-the-art baselines in terms of accuracy. I NTRODUCTION In recent years, recommendation systems gain increasing interest by both researchers and the industry [1], [2]. The most popular recommendation systems are based on collaborative filtering (CF) technique, which provides recommendations based on other similar users' choice [3]. Matrix factorization (MF) is one of the most common collaborative filtering models, whose main idea is to learn user latent vectors and item latent vectors, so that the inner product of the two vectors can approximate the original matrix with the minimal approximation error. MF has advantages of simplicity and performing well in many domains, such as recommendation systems, computer vision and document clustering [4]-[7]. However, it suffers from two limitations.

Nearly all established sports are going through some degree of hand-wringing over attracting younger fans as their older core ages out. The death of monoculture and explosion of entertainment options, many accessible without leaving one's bedroom, have seen attendance drops across the board. MLB and NFL teams have fallen over themselves installing on-site daily fantasy lounges to lure second-screeners. Even the hidebound International Olympic Committee has made transparent plays for youth, most recently with the addition of skateboarding, surfing and three-on-three basketball to next year's Summer Olympics in Tokyo. The demographic they're so thirsty for could be found in droves over the weekend at New York's Billie Jean King National Tennis Center, where three days of sold-out crowds turned out for the biggest video game competition of all time – the Fortnite World Cup – where a 16-year-old from Pennsylvania named Kyle Giersdorf (aka Bugha) brought home the winner's share of $3m with a dominant performance in Sunday's solos competition.

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.