Machine Learning


The Applications Of Machine Learning In Cyber-security ThinkPalm

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Machine Learning might be a department of computer science pointed at empowering computers to memorize unused behaviors based on experimental data. The objective is to plan the algorithms that allow a computer to show the behavior learned from past encounters, instead human interaction. Now we will examine applications of machine learning in cybersecurity and see how the machine learning algorithms offering assistance to us for battle with cyber-attacks. Machine learning (without human interaction) can collect analyze and prepare data. In cybersecurity, this innovation makes a big difference to analyze past cyber-attacks and create individual defense reactions.


We Need a Drastic Rethink on Export Controls for AI

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Dave Aitel is the founder and CTO of Immunity. You can follow him @daveaitel. Export control on AI and machine learning algorithms is becoming a more important part of national security strategy as the world moves to a great-power competition landscape and technological changes force accommodation and rapid change to many national interests. However, like security software before it, AI presents unique challenges to how export control has traditionally worked, and these should be considered before being codified into international regulatory frameworks. As an example, on January 6, 2020, The Bureau of Industry and Security (BIS) in the U.S. Department of Commerce released the following rule, which imposed a license requirement on a particular kind of software useful for automatically identifying objects from drone or other imagery: "Geospatial imagery "software" "specially designed" for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds, and having all of the following: Technical Note: A point cloud is a collection of data points defined by a given coordinate system. A point cloud is also known as a digital surface model."


Machine Learning with PySpark

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Spark is a powerful, general purpose tool for working with Big Data. Spark transparently handles the distribution of compute tasks across a cluster. This means that operations are fast, but it also allows you to focus on the analysis rather than worry about technical details. In this course you'll learn how to get data into Spark and then delve into the three fundamental Spark Machine Learning algorithms: Linear Regression, Logistic Regression/Classifiers, and creating pipelines. With this background you'll be ready to harness the power of Spark and apply it on your own Machine Learning projects!


Using Applied Machine Learning to Predict Healthcare Utilization Based on Socioeconomic Determinants of Care

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This study demonstrates that it is possible to generate a highly accurate model to predict inpatient and emergency department utilization using data on socioeconomic determinants of care. ABSTRACT Objectives: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. Study Design: The aim of this study was to demonstrate the influences of socioeconomic determinants of health (SDH) with regard to avoidable patient-level healthcare utilization. The study investigated the ability of machine learning models to predict risk using only publicly available and purchasable SDH data. A total of 138,115 patients were analyzed from a deidentified database representing 3 health systems in the United States.


Build a Textual Similarity Web App with TensorFlow.js

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Word embeddings enable knowledge representation where a vector represents a word. This improves the ability for neural networks to learn from a textual dataset. Before word embeddings were de facto standard for natural language processing, a common approach to deal with words was to use a one-hot vectorisation. Each word represents a column in the vector space, and each sentence is a vector of ones and zeros. As a result, this leads to a huge and sparse representation, because there are many more zeros than ones.


We know ethics should inform AI. But which ethics?

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Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore - and it's just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents.


Cybersecurity tool uses machine learning, honeypots to stop attacks

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In recent months, the FBI issued a high-impact cybersecurity warning in response to increasing attacks on government targets. Government officials have warned major cities such hacks are a disturbing trend likely to continue. Purdue University researchers may help stop some of those threats with a tool designed to alert organizations to cyberattacks. The system is called LIDAR – which stands for lifelong, intelligent, diverse, agile and robust. "The name for this architecture for network security really defines its significant attributes," said Aly El Gamal, an assistant professor of electrical and computer engineering in Purdue's College of Engineering.


Personal Invisibility Cloak Stymies People Detectors

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When Harry Potter receives an invisibility cloak as a Christmas gift he uses it to conceal himself from Hogwarts teachers and nasty caretaker Argus Filch. Now, researchers from Facebook AI and the University of Maryland have introduced a 21st century version -- sweatshirts printed with adversarial examples that make the wearer undetectable to the AI-powered object detectors in today's public surveillance systems. Ian Goodfellow, the renown research scientist who pioneered generative adversarial networks (GANs), describes adversarial examples as "inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake." In the new study, researchers printed adversarial examples on sweatshirts and other items to "attack" object detectors and cause them to fail to recognize their targets from images or videos. Fooling object detectors is much more difficult than fooling classifiers.


Capsule Network Performance with Autonomous Navigation -- Cousino Math

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Such convolutional neural networks are unable by their internal data representation struggle to maintain spatial hierarchies between simple and complex objects. Whereas capsule networks, which encode their data as vectors, can encode the probability of feature detection as the magnitude of the vector and the state of the detected feature in the direction of the vector. So a detected feature that moves around will have its associated vector maintain the same magnitude throughout the movement but alter their vector's orientation. Via dynamic routing, a capsule network sends lower-level capsule outputs to higher-level capsules with similar outputs--where the dot product measures similarity of vector outputs. The task of autonomous navigation is one of reinforcement learning.


Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches

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Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progressive lung damage and represent the primary cause of morbidity and mortality. Staphylococcus aureus (S. aureus) is one of the earliest bacteria in CF infants and children. Starting from early adolescence, patients become chronically infected with Gram-negative non-fermenting bacteria, and Pseudomonas aeruginosa (P. Intensive use of antimicrobial drugs to fight lung infections inevitably leads to the onset of antibiotic resistant bacterial strains. New antimicrobial compounds should be identified to overcome antibiotic resistance in these patients.