Basic Level
AI and the Future of Skills, Volume 1
The OECD launched the Artificial Intelligence and the Future of Skills project to develop a programme that could assess the capabilities of AI and robotics and their impact on education and work. This report represents the first step in developing the…
George Boole: the father of logic, bases digital electronics and the substrate of binary language and a research
Without he, neither electronics, computing or Artificial Intelligence would be what they are.
Working from home during the #COVID19 crisis
The EU Agency for #Cybersecurity (ENISA) shares its cybersecurity recommendations on working remotely during the COVID-19 crisis.
Statistics Using Excel Succinctly
Learn the ins and outs of Microsoft Excel’s statistical capabilities. Author Charles Zaiontz will help you familiarize yourself with an often overlooked but very powerful set of tools. With Statistics Using Excel Succinctly, you will be able to maximize your Excel…
Defining Artificial Intelligence |🇪🇸 Definiendo Inteligencia Artificial
The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence. To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language…
Statistics Fundamentals Succinctly
Statistics is the foundation of intelligent data analysis. Statistics Fundamentals Succinctly by Katie Kormanik provides the foundational bricks and mortar needed to master the theories and methodologies behind statistical procedures. In less than 100 pages, you’ll understand how to better gather…
Artificial Intelligence in Society
«The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global…
Machine learning in Python: the top programming language
Phyton’s most notable points are:
-Is a great library ecosystem (Scikit-learn, Pandas, Matplotlib, NLTK, Scikit-image, PyBrain, Caffe, StatsModels, TensorFlow, Keras, etc).
-Growing popularity.
-Has a low entry barrier, has flexibility, is a platform independence, has readability, good visualization options, good community support and…
LaTeX Notes for professionals book
This LaTeX Notes for professionals book is compiled from Stack Overflow Documentation. (60 pages, published on May 2018)
La era de los datos | 🇬🇧 The Age of Data
La era de los datos | 🇬🇧 The Age of Data
«Diecinueve grandes expertos de todo el mundo esbozan las reformas ambiciosas y radicales necesarias para encarar los desafíos de la era de los datos». | 🇬🇧 «Nineteen leading experts from…
La Organización Mundial de la Propiedad Intelectual cierra la consulta pública sobre políticas de Inteligencia Artificial y Propiedad Intelectual
La OMPI ha recibido más de 250 respuestas a su solicitud de comentarios públicos para un borrador de documento sobre cuestiones sobre propiedad intelectual e inteligencia artificial. Y ha recopilado una amplia gama de partes interesadas de todo el mundo.
A European strategy for data
The EU can become a leading role model for a society empowered by data to make better decisions -in business and the public sector. To fulfil this ambition, the EU can build on a strong legal framework – in terms of…
WHITE PAPER On Artificial Intelligence – A European approach to excellence and trust
The purpose of this White Paper is to set out policy options on how to achieve these objectives. It does not address the development and use of AI for military purposes.The Commission invites Member States, other European institutions, and all stakeholders,…
Learning Scala Language
Learning Scala Language eBook. (Stack Overflow Documentation).
Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. It smoothly integrates features of object-oriented and functional languages.
R Notes for Professionals book
This R Notes for Professionals book is compiled from Stack Overflow Documentation. (475 pages, published on May 2018)
Python® Notes for Professionals book
This Python® Notes for Professionals book is compiled from Stack Overflow Documentation. (816 pages, published on June 2018)
Forecasting: Principles and Practice
A comprehensive introduction to the latest forecasting methods. Examples use R with many data sets taken from the authors’ own consulting experience. In this second edition, all chapters have been updated to cover the latest research, and three new chapters have…
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
Artificial Intelligence and Public Standards (A Review by the Committee on Standards in Public Life) -GOV.UK
The Committee on Standards in Public Life Published (10 February 2020 ) its report and recommendations to the Prime Minister to ensure that high standards of conduct are upheld as technologically assisted decision making is adopted more widely across the public…
Manifiesto de IBM con motivo del 50 encuentro en el foro de Davos. ‘Regulación de precisión para la inteligencia artificial’
El panel de Davos, organizado por el CEO de IBM, Ginni Rometty, explora la regulación de precisión de la inteligencia artificial y la tecnología emergente. El evento lanza formalmente IBM Policy Lab, un nuevo foro para promover recomendaciones de políticas audaces…
Estudio prospectivo de políticas sobre inteligencia artificial y política comercial de la UE
El Ministerio holandés de Asuntos Exteriores ha encargado este estudio para generar conocimiento sobre la interfaz entre el derecho comercial internacional y las normas europeas y valores en el uso de la inteligencia artificial. El estudio hace una serie de…
La inteligencia artificial como ente jurídico y su responsabilidad civil en el seno de Europa
«No es necesario dar a los dispositivos o sistemas autónomos una personalidad jurídica, ya que el daño que pueden causar puede y debe ser atribuible a personas u organismos existentes». (Report from the Expert Group on Liability and New Technologies…
Una definición de Inteligencia Artificial: principales capacidades y disciplinas científicas
Este documento amplía la definición de Inteligencia Artificial (IA) tal como se define en la Comunicación de la Comisión sobre IA. Aclara ciertos aspectos de la IA como disciplina científica y como tecnología, con el objetivo de evitar malentendidos, para lograr…
Ley: diseño de Inteligencia Artificial éticamente alineado (IEEE)
EAD, Primera edición, incluye comentarios sobre cómo la ley debe responder a una serie de desafíos éticos y legales específicos que plantea el desarrollo y despliegue de A/IS (Autonomous and Intelligent Systems) en la vida contemporánea. También se centra en el…
Realidad Virtual (RV) & Realidad Aumentada (RA) & Realidad Mixta (RM)
La realidad en estos tiempos comienza a ser casi como un espejismo de nosotros mismos. Se está convirtiendo en un anhelo de mejoras artificiales entorno a nuestra sociedad y a los seres humanos. Ya no nos sorprende nada…
Medium Level
Matriz de recursos de Deep Learning
El siguiente recurso describe los siguientes marcos:
TensorFlow
Theano
Cafe
MXNet
apache
SystemML (proyecto de incubadora)
BigDL
DistBelief
C++ Notes for Professionals book
C++ Notes for Professionals book is compiled from Stack Overflow Documentation,. 708 pages, published on May 2018
AutoML: a introduction tutorial about H2O Driverless AI
«H2O has been the driver for building models at scale. We are talking about billions of claims. You can’t do this with standard off the shelf open source techniques». (H2o.ai).
Resources on learning deeplearning from the Massachusetts Institute of Technology
A great selection of resources on learning deeplearning by Lex Fridman from the Massachusetts Institute of Technology (MIT).
Python for Everybody: Exploring Data in Python 3
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet. (Dr. Charles R….
Natural Language Processing Succinctly
AI assistants represent a significant frontier for development. But the complexities of such systems pose a significant barrier for developers. In Natural Language Processing Succinctly, author Joseph Booth will guide readers through designing a simple system that can interpret and provide reasonable responses to written…
Natural Language Processing with Python
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library.
Scikit-Learn
Scikit-Learn Tutorial (Materials for my scikit-learn tutorial)
By Jake VanderPlas who is the director of Open Software at the University of Washington’s eScience institute, and researches and teaches in a variety of areas, including Astronomy, Astrostatistics, Machine Learning, and Scalable Computation.
Neural Networks with JavaScript Succinctly
Although most concepts are relatively simple, there are many of them, and they interact with each other in unobvious ways, which is a major challenge of neural networks. But you can learn all important neural network concepts by running and examining the code in this…
JavaScript® Notes for Professionals book
This JavaScript® Notes for Professionals book is compiled from Stack Overflow Documentation. (490 pages, published on June 2018)
Java® Notes for Professionals book
This Java® Notes for Professionals book is compiled from Stack Overflow Documentation. (982 pages, published on June 2018)
Python Data Science Handbook (Essential Tools for Working with Data)
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython,…
MATLAB® Notes for Professionals book
This MATLAB® Notes for Professionals book is compiled from Stack Overflow Documentation. (182 pages, published on May 2018)
A Whirlwind Tour of Python
A Whirlwind Tour of Python is a fast-paced introduction to essential features of the Python language, aimed at researchers and developers who are already familiar with programming in another language. The material is particularly designed for those who wish to use Python for data science…
R for Data Science
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills…
Select Star SQL
This is an interactive book which aims to be the best place on the internet for learning SQL. It is free of charge, free of ads and doesn’t require registration or downloads. It helps you learn by running queries against a real-world dataset to complete…
Turing-NLG: A 17-billion-parameter language model by Microsoft
Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. We present a demo of the model, including its freeform generation, question answering, and summarization capabilities, to academics for…
Cyber Attack Detection thanks to Machine Learning Algorithms
Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used
and recommended, are no longer sufficient…
Deep Learning
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. (“Written by three…
Free and Open Machine Learning Documentation Release 1.0.1
This book is all about applying machine learning solutions for real practical use cases. This means the core focus is on outlining how to use machine learning in a simple way so you can benefit of this powerful technology.
Machine learning is an exciting and powerful…
People + AI Guidebook by Google
Escrita para profesionales de experiencia de usuario (UX) y gerentes de producto como una forma de ayudar a crear un enfoque centrado en el ser humano para la IA en sus equipos de producto.
Guía de diseño de COBIT 2019
Guía de diseño de COBIT 2019: el diseño de una solución de gobernanza de la información y la tecnología es una publicación innovadora para el marco de trabajo de COBIT.
Advanced Level
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without…
Human Learn
Machine learning covers a lot of ground but it is also capable of making bad decision. We’ve also reached a stage of hype that folks forget that many classification problems can be handled by natural intelligence too. This package contains scikit-learn compatible tools that should…
Addressing Ethical Dilemmas in AI: Listening to Engineers
Documentation is key – design decisions in AI development must be documented in detail, potentially taking inspiration from the field of risk management. There is a need to develop a framework for large-scale testing of AI effects, beginning with public tests of AI systems, and…
Tidy Modeling with R
This book provides an introduction to how to use our software to create models. We focus on a dialect of R called the tidyverse that is designed to be a better interface for common tasks using R. If you’ve never heard of or used the…
The future of AI
If you wonder what is next in the evolution towards general AI then this session is for you. We have seen some painful failures of artificial intelligence pointing to a lack of ‘common sense’. Are neural networks really the solution we seek or is a…
Machine Learning from scratch (by Danny Friedman)
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool…
A short tour of Scikit-image library with hands-on code
In this short book, we illustrate some of the core algorithms/functions of this popular Python library for image processing and manipulation tasks, with hands-on code examples.
Artificial Intelligence in Medical Imaging
«This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging».
Mastering Shiny
This book complements Shiny’s online documentation and is intended to help app authors develop a deeper understanding of Shiny. After reading this book, you’ll be able to write apps that have more customized UI, more maintainable code, and better performance and scalability.
Privacy Preserving AI – Andrew Trask, OpenMined
Learn the basics of secure and private AI techniques, including federated learning and secure multi-party computation. In this talk, Andrew Trask of OpenMined highlights the importance of privacy preserving machine learning, and how to use privacy-focused tools like PySyft.
Interpretable Machine Learning (A Guide for Making Black Box Models Explainable)
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine…
Unconventional Computer Arithmetic for Emerging Applications and Technologies
Arithmetic plays a major role in computing performance and efficiency. It is challenging to build platforms, ranging from embedded devices to high performance computers, supported on traditional binary arithmetic and silicon-based technologies that meet the requirements of today’s applications. In this talk, the state-of-the-art of…
Trainings for Cybersecurity Specialists
«ENISA CSIRT training material was introduced in 2008. In 2012, 2013 and 2014 it was complemented with new exercise scenarios containing essential material for success in the CSIRT community and in the field of information security. In these pages you will find the ENISA CSIRT…
Best Practices in Dataviz: An R Perspective
By the end of this you will have had a whirlwind tour of the very tip of the data visualization best-practices iceberg. We will go over a broad range of topics generally applicable to data science usecases but not dive too deep into any single…
Cumplimiento normativo y seguridad TI en IoT
La conectividad y el tratamiento masivo de datos son dos pilares esenciales para el desarrollo de estos sistemas, los cuales, a su vez, introducen riesgos de seguridad y privacidad que deben ser tratados adecuadamente.
CSIRTs and criminal justice authorities
CSIRTs and criminal justice authorities – good practices of collaboration on cybercrime and electronic evidence (webinar, Council Europe)
Google Engineering Practices Documentation
Google has many generalized engineering practices that cover all languages and all projects. These documents represent their collective experience of various best practices that they have developed over time. It is possible that open source projects or other organizations would benefit from this knowledge.
IEEE: Certification Program for Autonomous and Intelligent Systems (ECPAIS) is ready for phase II
#IEEE Invites Companies, Governments and Other Stakeholders Globally to Expand on #Ethics #Certification Program for #Autonomous and #Intelligent #Systems (#ECPAIS) Work
Encrypted Traffic Analysis
This report explores the current state of affairs in Encrypted Traffic Analysis and in particular discusses research and methods in 6 key use cases; viz. application identification, network analytics, user information identification, detection of encrypted malware, file/device/website/location fingerprinting and DNS tunnelling detection.
Cyber Europe 2020: Preparing healthcare sector to respond to cyber crises
In 2020, European countries and the EU Agency for Cybersecurity (ENISA) should have organised the 6th pan European cyber crisis exercise, Cyber Europe 2020 (CE2020). However, due to the current health crisis Cyber Europe will be shifted to a later date, yet to be announce….
EUvsVirus, a pan-European hackathon: When? 24, 25 and 26 April
The European Commission, in close collaboration with EU member states, will host a pan-European hackathon to connect civil society, innovators, partners and buyers across Europe to develop innovative solutions to overcome coronavirus-related challenges.
Advanced R
Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book…
IPython Interactive Computing and Visualization Cookbook
IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code.
Data Science at the Command Line
Today, data scientists can choose from an overwhelming collection of exciting technologies and programming languages. Python, R, Hadoop, Julia, Pig, Hive, and Spark are but a few examples. You may already have experience in one or more of these. If so, then why should you…
Stanza – una biblioteca de Python NLP para muchos idiomas humanos
El diseño del kit de herramientas permite trabajar en paralelo entre más de 70 idiomas, utilizando el formalismo de Dependencias Universales. Stanza está construido con componentes de red neuronal de alta precisión, que también permiten una capacitación y evaluación eficientes con sus propios datos anotados.
Algorithms Notes for Professionals
Algorithms Notes for Professionals book is compiled from Stack Overflow Documentation. (252 pages, published on May 2018)
Keras Succinctly
Neural networks are a powerful tool for developers, but harnessing them can be a challenge. With Keras Succinctly, author James McCaffrey introduces Keras, an open-source, neural network library designed specifically to make working with backend neural network tools easier.
Efficient R programming
There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively-until now.
TPOT is a Python Automated Machine Learning tool
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning (AutoML) tool that optimizes machine learning pipelines using genetic programming.
Scikit-Learn
Scikit-Learn Tutorial (Materials for my scikit-learn tutorial)
By Jake VanderPlas who is the director of Open Software at the University of Washington’s eScience institute, and researches and teaches in a variety of areas, including Astronomy, Astrostatistics, Machine Learning, and Scalable Computation.
A quantum engineer’s guide to superconducting qubits
Discussed the phenomenalprogress over the last decade in the engineering of superconducting devices, the development of high-fidelitygate operations, and quantum non-demolition measurements with high signal to noise ratio.
Machine Learning with dabl ( 0.1.8)
«dabl tries to reduce the turnaround time required for a quick baseline estimate of a supervised learning problem. It does so by automating the task of iterating through different techniques of data preprocessing, feature engineering, parameter tuning and model building to generate efficacious baseline models».
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield…
El Programa de Certificación de Ética para Sistemas Autónomos e Inteligentes (ECPAIS)
El objetivo del Programa de Certificación de Ética para Sistemas Autónomos e Inteligentes (ECPAIS) del IEEE SA es crear especificaciones para los procesos de certificación y marcado que promuevan la transparencia, la rendición de cuentas y la reducción del sesgo algorítmico en los Sistemas Autónomos…
Research Level
An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
Sequential recommendation is a central task in recommender systems, and recent research has increasingly shifted toward generative recommenders that leverage both sequential patterns and semantic item information. However, these methods are often evaluated on a small set of widely used benchmarks. This raises a natural…
When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare
Large language models (LLMs) are designed to generate answers to user prompts, which often drives them to respond even when uncertainty is high, information is incomplete, or a refusal would be more appropriate. In healthcare, this tendency can be dangerous: confidently stated but inaccurate medical…
Advancing regulatory variant effect prediction with AlphaGenome
Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance1,2,3,4,5. We present AlphaGenome, a unified DNA…
Sequence Feature Extraction for Malware Family Analysis via Graph Neural Network
Malicious software (malware) causes much harm to our devices and life. We are eager to understand the malware behavior and the threat it made. Most of the record files of malware are variable length and text-based files with time stamps, such as event log data…
Auto Quantum Circuits
«AutoQML, self-assembling circuits, hyper-parameterized Quantum ML platform, using cirq, tensorflow and tfq. Trillions of possible qubit registries, gate combinations and moment sequences, ready to be adapted into your ML flow. Here I demonstrate climatechange, jameswebbspacetelescope and microbiology vision applications… [Thus far, a circuit with 16-Qubits and a gate sequence…
Federated Learning: Issues in Medical Application
In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various…
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
In this survey, we connect several lines of work from the pre-neural and neural era, by showing how hybrid approaches of words and characters as well as subword-based approaches based on learned segmentation have been proposed and evaluated. We conclude that there is and…
Scientific Visualization: Python + Matplotlib
The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are…
Ethics-based auditing of automated decision-making systems: intervention points and policy implications
Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee…
Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these…
The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming
We analyze the math fundamentals behind DP and demonstrate the power of it by applying it on two real-world text classification tasks. Furthermore, we compare DP with pointillistic active and semi-supervised learning techniques traditionally applied in data-sparse settings.
Highly accurate protein structure prediction with AlphaFold
Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
Model-based Decision Making with Imagination for Autonomous Parking
Autonomous parking technology is a key concept within autonomous driving research. This paper will propose an imaginative autonomous parking algorithm to solve issues concerned with parking.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
CARLA (Counterfactual And Recourse LibrAry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework…
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
This paper provides a succinct overview of this emerging theory of overparameterized ML (henceforth abbreviated as TOPML) that explains these recent findings through a statistical signal processing perspective. We emphasize the unique aspects that define the TOPML research area as a subfield of modern ML…
How to avoid machine learning pitfalls: a guide for academic researchers
This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular…
YOLOX: Exceeding YOLO Series in 2021
We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G…
Framework based on parameterized images on ResNet to identify intrusions in smartwatches or other related devices
The continuous appearance and improvement of mobile devices in the form of smartwatches, smartphones and other similar devices has led to a growing and unfair interest in putting their users under the magnifying glass and control of applications.
YOLOP: You Only Look Once for Panoptic Driving Perception
A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation…
labml.ai Deep Learning Paper Implementations
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.
Data as the main focus of “State of the art of data science in Spanish language and its application in the field of Artificial Intelligence”
According to the results, there is an evidence of cultural bias for data science in Spanish language. The outcome of the consultation, which carried out on 12 April 2021, confirms that only 10 out of 23.771 datasets “speaks” Spanish.»
‘Framework’ basado en imágenes parametrizadas sobre ResNet para identificar intrusiones en ‘smartwatches’ u otros dispositivos afines
La continua aparición y mejora de dispositivos móviles en forma de ‘smartwatches’, ‘smartphones’ y otros dispositivos similares ha propicio un creciente y desleal interés en poner bajo la lupa y el control de los aplicativos a sus usuarios. De forma ofuscada por los fabricantes.
Los datos como eje principal en el «Estado del arte de la ciencia de datos en el idioma español y su aplicación en el campo de la Inteligencia Artificial»
Los resultados de este estudio son una evidencia del sesgo cultural que existe entre la lengua inglesa y la española en la ciencia de datos. De los 23.771 conjuntos de datos que se encontraron con fecha de consulta 12/04/2021, tan solo 10 se encontraban en…
EvalML: a library for automated machine learning and model understanding
EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python
State of the art of data science in Spanish language and its application in the field of AI
The study of art provides results that indicate the absence of involvement of Spanish language with AI and all the subareas, which consequently adversely affect to the education of future professionals.
Partial Differential Equations is All You Need for Generating Neural Architectures — A Theory for Physical Artificial Intelligence Systems
In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research
Federated Quantum Machine Learning
We present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model.
El estado del arte de la ciencia de datos en el idioma español y su aplicación en el campo de la Inteligencia Artificial
El estudio arroja resultados que indican la falta de involucración del Español con la IA así como de todas las subáreas, afectando negativamente a la formación de futuros profesionales.
Classification based on Topological Data Analysis
Topological Data Analysis (TDA) is an emergent field that aims to discover topological information hidden in a dataset. TDA tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes an algorithm that applies TDA directly…
Hugging Face datasets
One-line dataloaders for many public datasets & Efficient data pre-processing
S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural…
Multi-Image Steganography Using Deep Neural Networks
Steganography is the science of hiding a secret message within an ordinary public message. Over the years, steganography has been used to encode a lower resolution image into a higher resolution image by simple methods like LSB manipulation. We aim to utilize deep neural networks…
El arte de la Inteligencia Artificial desde una perspectiva léxica
El principal objetivo de este documento es construir un glosario, a partir de las propuestas léxicas realizadas por los diferentes entes tecnológicos (ISO, IEEE, Wikipedia y Oxford University Press). Adicionalmente, el glosario estará estructurado según las ramas de conocimiento de esta área de trabajo, determinando…
Explainability in Graph Neural Networks: A Taxonomic Survey
We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
«We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation».
Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring
Information within systems can be extracted through side-channels; unintended communication channels that leak information. The concept of side-channel sensing is explored, in which sensor data is analysed in non-trivial ways to recover subtle, hidden or unexpected information.
Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric…
Evaluating and Characterizing Human Rationales
Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.
Medical notes summariser: «Characterizing the Value of Information in Medical Notes»
Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes.
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building…
WHAT IS [MEANINGFUL PRIVACY]*
Meaningful privacy and how it is applied in technology will be the focus of 60 privacy preserving leaders from around the globe during the OpenMined Privacy conference Sept 26 and 27 2020 with more than 2000 in attendance virtually.
Report on Publications Norms for Responsible AI
The history of science and technology shows that seemingly innocuous developments in scientific theories and research have enabled real-world applications with significant negative consequences for humanity.
COCIR analyses application of medical device legislation to Artificial Intelligence
«The European Commission has shown its ambition in the area of artificial intelligence (AI) in its recent White Paper on Artificial Intelligence – a European approach to excellence and trust. This White Paper is at the same time a precursor of possible legislation of AI…
IEEE Use Case–Criteria for Addressing Ethical Challenges in Transparency, Accountability, and Privacy of CTA/CTT
There are substantial public health benefits gained through successfully alerting individuals and relevant public health institutions of a person’s exposure to a communicable disease. Contact tracing techniques have been applied to epidemiology for centuries, traditionally involving a manual process of interview and follow-up. This is…
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack’s modular design enables researchers to easily construct attacks…
Research and innovation in smart mobility and services in Europe
For smart mobility to be cost-efficient and ready for future needs, adequate research and innovation (R&I) in this field is necessary. This report provides a comprehensive analysis of R&I in smart mobility and services in Europe.
Diagnostic uncertainty calibration: towards reliable machine predictions in medical domain
We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over class probability…
Transforming Health Care Through AI Revolutions
I will discuss relevant AI thrusts at NIST on health care informatics, focusing on the use of machine learning, knowledge representation and natural language processing. I will also discuss the need for explanations in AI systems (XAI) and current state of the art in medical…
Disposable Identities are Elemental(s) in IoT
Rob wants to argue that if intent is linked to an incorrect assessment of identity, and thus not central to an ethics of behaviour, then this opens up an actionable set of actors actually at play in the digtial (IoT, 5G, AI) namely: objects (with…
The State of AI Ethics Report (June 2020)
It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring…