uk: Комп'ютерні науки

description: The Blue Team Village is proud to introduce the Meet-a-Mentor program. Come meet the Core Mentors and learn how you can take part of the program as either mentor or mentee. The Meet-a-Mentor program seeks to bring together mentors and mentees based on shared experiences and areas of interest in order to develop the mentee’s goals and career skills, help them address and overcome challenges in their career and/or workplace, and also to broaden the mentor’s perspectives and mentoring skills.

uk: вхід без авторизації

name: Blue Team Village - Introducing the Meet-A-Mentor Program


Blue Team Village - QuarkEngine

JunWei Song

type: Person

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: Android malware analysis engine is not a new story. Every antivirus company has their own secrets to build it. With python and curiosity, we develop a malware scoring system from the perspective of Taiwan Criminal Law in an easy but solid way.

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

DEF CON

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


Blue Team Village - Intro to Zeek

uk: Комп'ютерні науки

description: Zeek is an Open Source Network Security Monitoring Tool. Zeek is not an active security device, like a firewall or intrusion prevention system. Rather, Zeek sits on a “sensor,” a hardware, software, virtual, or cloud platform that quietly and unobtrusively observes network traffic. We're going to give you some resources on how to deploy Zeek, but spend most of our time looking at Zeek data and interpreting what it's telling us about real-world network traffic and attacks. No VMs required, just come as you are! Every year the Blue Team Village hosts OpenSOC. A unique defense CTF meant to teach and test practical incident response skills in an environment that's as close to "the real thing" as it gets. This year BTV wanted to do more. We know that some Blue Teamers might be unfamiliar with some of the tools used by OpenSOC. And we didn't want that to keep anyone from playing this incredible defense simulation. So this year we are dedicating all day Thursday to demo the various OpenSOC tools, before OpenSOC starts on Friday. These are tools like Graylog, Moloch, Zeek, Osquery, and others that Blue Teamers rely on every day to defend their networks against attackers. That means that after you LEARN the tools, you can PLAY the OpenSOC CTF, and then take that knowledge back to your own Blue Team to DO the work of defending your network.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Too often we just kind of wait for a good idea to hit, without any deliberate method to harvest it.

uk: вхід без авторизації

name: Deliberate creativity: Thinking, programming, and inventing in the 21st Century


An Overview on Encryption in C++

uk: Комп'ютерні науки

description: Encryption has become a very important topic for C++ developers and this session will serve as an introduction and overview this topic. This overview will include basic concepts such as symmetric vs. asymmetric encryption. We'll also covers some known algorithms and their implementations in cryptopp, botan and libsodium.

uk: вхід без авторизації


F# as our day job by 2016

uk: Комп'ютерні науки

description: F# is a language I am passionate about and a language I would like to use on my day job, and for that to be a reality I need you to join me! I want you to join me in the welcoming F# community where you can see a new world open up for you and enabling you to solve problems more efficiently than you do today with C#. F# is not a language that is used only in academia, it is a general purpose language that is growing every day with an awesome community of brilliant people. F# also defines the future of C# in many ways, so if you want to be ahead of the game you should jump on the F# bandwagon today. There are many reasons to learn F# like; a new paradigm will make you a better overall developer, F# will make you write less bugs and you will have more fun at work!

uk: вхід без авторизації


History and Spirit of C and C++

uk: Комп'ютерні науки

description: To get a deep understanding of C and C++, it is useful to know the history of these wonderful programming languages. It is perhaps even more important to appreciate the driving forces, motivation and the spirit that has shaped these languages into what we have today. In the first half of this talk we go back to the early days of programmable digital computers. We will take a brief look at really old machine code, assembler, Fortran, IAL, Algol 60 and CPL, before we discuss the motivations behind BCPL, B and then early C. We will also discuss influential hardware architectures represented by EDSAC, Atlas, PDP-7, PDP-11 and Interdata 8/32. From there we quickly move through the newer language versions such as K&R C, C89, C99 and C11. In the second half we backtrack into the history again, now including Simula, Algol 68, Ada, ML, Clu into the equation. We will discuss the motivation for creating C++, and with live coding we will demonstrate by example how it has evolved from the rather primitive ³C with Classes² into a supermodern and capable programming language as we now have with C++11/14 and soon with C++17.

uk: вхід без авторизації


Making Hacking Child’s Play

uk: Комп'ютерні науки

description: How does it keep happening? I mean why are websites so consistently hacked? Is it the work of sophisticated cyber-villains operating from underground lairs? As it turns out, many of today’s online attacks are mounted by kids – legally children – who simply have time, patience and access to Google. In this session we’re going to look at and execute a heap of online attacks with tools that even kids can use. You’ll learn just how easy it is to mount these attacks and most importantly, what it is you need to do to keep your apps secure not just from mischievous kids, but from cyber-criminals and even nation state actors. This talk is scary, entertaining and will make you distrusting of any kid with a PC.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Microservcies is the latest industry buzzword, but it's definition seems to be shifting and elusive. In this presentation we will cut through the hype and debate to expose the underlying original ideas that form the pillars upon which the Microservices idea is built. With an understanding of those pillars we will look at what makes Microservices different from it's predecessors, what we need to do to deliver on those differences and how to leverage them to support agile delivery. Finally we will look at impementation, with a focus on libraries and frameworks that can help us deliver Microservices under .NET.

uk: вхід без авторизації

name: Microservices, cutting through the Gordian Knot


uk: Комп'ютерні науки

description: In this bleak, relentlessly morbid talk, James Mickens will describe why making computers secure is an intrinsically impossible task. He will explain why no programming language makes it easy to write secure code. He will then discuss why cloud computing is a black hole for privacy, and only useful for people who want to fill your machine with ads, viruses, or viruses that masquerade as ads. At this point in the talk, an audience member may suggest that Bitcoins can make things better. Mickens will laugh at this audience member and then explain why trusting the Bitcoin infrastructure is like asking Dracula to become a vegan. Mickens will conclude by describing why true love is a joke and why we are all destined to die alone and tormented. The first ten attendees will get balloon animals, and/or an unconvincing explanation about why Mickens intended to (but did not) bring balloon animals. Mickens will then flee on horseback while shouting “The Prince of Lies escapes again!”

uk: вхід без авторизації

name: Not Even Close - The State of Computer Security


To SQL or NoSQL?: That Is the Question

uk: Комп'ютерні науки

description: Everybody is talking about big data, and NoSQL technologies are all the rage these days. But do you really have "big data", or is your database server just slow? Should you take the plunge into the deep end of the NoSQL pool, or just use a relational database? If you do start using NoSQL, which of the many different systems best fits your particular use case? And how do you transition your existing infrastructure and code to a whole new, conceptually different, storage mechanism? All of these questions and more will be answered in this session. You'll learn about the various families of NoSQL data stores, their pros and cons, and which problems each one is designed to solve. You'll get a better understanding of when to use relational databases, when to use NoSQL and when to use a polyglot combination of storage technologies. You'll hear about ways to plan and execute staged system migrations from relational to NoSQL databases. And finally, you'll find out how to search and analyze your stored data quickly and efficiently.

uk: вхід без авторизації


When order does not matter

uk: Комп'ютерні науки

description: When programming in C++ arguments to function calls are always passed in a predefined order with a type checking. passing them in a wrong order or using a wrong type for them will typically result in a compilation error. This is a feature... most of the time. There are situations when possibility to change order or not to pass some arguments (not necessary the last one(s)) is a huge advantage. during the presentation a case study for such a situation will be presented, along with a template-based solution, using standard modern C++.

uk: вхід без авторизації


cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

uk: вхід без авторизації

name: Organization Nikolov, Aleksandar (Moderation)

name: Curve Based Approximation of Images on Manifolds

name: Banff International Research Station (BIRS) for Mathematical Innovation and Discovery


keywords:
OpenDataScience
Open Data
Land cover mapping

uk: Комп'ютерні науки

description: Martin Herold is Professor of Geoinformation Science and Remote Sensing, Wageningen University. In this interview, we asked Martin to share his thoughts around the challenges and opportunities of Open Data science in solving the greatest challenges of our times.

uk: вхід без авторизації

name: OpenDataScience Europe 2021: Interview with Martin Herold: Global land cover mapping and assessments


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

en: Information and Library Sciences

de: Allgemeine und vergleichende Literatur- und Sprachwissenschaft

uk: Загальне та порівняльне літературознавство та мовознавство

en: General and Comparative Literary Studies and Linguistics

de: Geisteswissenschaften

uk: Гуманітарні науки

en: Humanities

uk: вхід без авторизації

name: Ensuring Public Access to Research Data: Perspectives from Three Academic Research Libraries


How Nix grew a marketing team

uk: Комп'ютерні науки

description: In my talk I'd like to go over: Why Nix community needs a marketing team What have we been up to in marketing team in the last 6 months What are the plans for the next 6 months Marketing, and non-technical work in general, is all too often an afterthought for developers or worse it is viewed as something negative. I really wish it weren’t the case. Having clearly defined problems, audience and strategy should be as important to us as having clean and tested code. This is important for Nix. This is important for any project that aims to succeed.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: The structures of drugs and their delivery mechanisms have become orders of magnitude more sophisticated in recent years. Polymer subdermal trickle-delivery implants can now be manufactured with a 3D printer filament extruder. We can now find simple new synthesis pathways for complex molecules using machine learning systems, and these compounds can be made at home. The Four Thieves Vinegar Collective will show the free, open access, supercomputing platform they have built so that anyone can do research in this arena independently on our hardware. Additionally, they will show the latest version of the automated chemical reactor, the Apothecary Microlab, which requires no soldering, and is built entirely from off-the-shelf and 3D printed parts.

uk: вхід без авторизації

name: Biohacking Village - Making Next Generation Drugs at Home: Four Thieves vinegar collective


uk: Комп'ютерні науки

description: The National Institutes of Health’s (NIH) All of Us Research Program (AoURP) aims to enroll at least one million US participants from diverse backgrounds; collect electronic health record (EHR) data, survey data, physical measurements, biospecimens for genomics and other assays, and digital health data; and create a researcher database and tools to enable precision medicine research. Since inception, digital health technologies (DHT) have been envisioned as integral to achieving the goals of the program. A “bring your own device” (BYOD) pilot for collecting Fitbit data from participants’ devices was developed with more recent integration of Apple HealthKit data donation and additional DHTs planned in the future. This presentation will describe 1) the initial process to assess, curate, and include Fitbit BYOD data in the All of Us Researcher Workbench, 2) the diversity and assessment of under-represented in biomedical research (UBR) in Fitbit BYOD participants compared with overall AoU participant population, and 3) future DHT studies planned for AoURP.

uk: вхід без авторизації

name: Biohacking Village - The All of Us Research Program: Digital Health Technologies (DHT)


uk: Комп'ютерні науки

description: Chinese Military Labratory Mission + COVID-19 discusses respectful research regarding military labs in the People's Republic of China and potential implications for weaponizing viruses, such as COVID-19. Audience will receive a filled experienced trip in the Chinese Military bio-weapons programs

uk: вхід без авторизації

name: Biohacking Village - Chinese Military Lab Mission + COVID-19


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Biohacking Village - How to Grow a Brain in a Jar: Neuroengineering 101


uk: Комп'ютерні науки

description: A group of mostly volunteer researchers in several biohacking labs around the world are working on novel protocols and open source equipment to produce insulin in hopes of reducing the cost of the drug, improving access to it, and involving more people in its production. and in research on diabetes treatments and medicine in general. Insulin, despite being known and used as a treatment for diabetes for almost 100 years, remains out of reach of roughly 50% of the 100 million worldwide who need it, according to the 100 Campaign. Even in the developed world, particularly the US, high costs prevent many patients from having enough, leading to severe health consequences, including nerve and kidney damage, cardiovascular disease, amputations, coma, and death. Anthony will speak about the history and the personal, philosophical and economic motivations of the Open Insulin project, and the context established by other patient-led R&D efforts in the diabetes community and the open source community. Then he will describe the technical and organizational aims of the project and the progress to date, and how to get involved.

uk: вхід без авторизації

name: Biohacking Village - Hacking the Insulin Supply Chain


uk: Комп'ютерні науки

description: Deeply understanding the risks that confront organizations is critical for their continuation and success. While many regulatory requirements mandating risk assessments exist, there’s a dearth of consistent and scalable real-world methodologies that can rigorously shape an assessment, let alone one common between disciplines like privacy and security. This presentation will introduce the Threat Modeling process Omada Health developed for its internal use (called INCLUDES NO DIRT), based on models common to various sectors and designed to integrate the perspectives of privacy, cybersecurity, and regulatory compliance for increased effectiveness and efficiency. We’ll explain briefly how (and why!) we developed the process, how it works in practice, and take a relevant example (a combination of a device type and a health coaching process) to show the Threat Model in action.

uk: вхід без авторизації

name: Biohacking Village - Biohacking Risks: Down and Dirty Threat Modeling for Digital Healthcare and Beyond


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Biohacking Village - Infodemic: Threat Models for patient communities on social networks.


uk: Комп'ютерні науки

description: Hear some of the top Security Researchers share their trials and tribulations with Medical Device Manufacturers. Topics will include: How they have succeeded in their interactions with larger organizations and what they are working on next. The success stories and failures of working with Medical Device Manufacturers. How working with diverse backgrounds and being open to researchers has helped one company mature its cybersecurity program. What is a proactive security approach and how can it help anticipate failure? How can we tackle the legacy device problem? Explore new ways the community can bring innovative solutions to Medical Device Security.

uk: вхід без авторизації

name: Biohacking Village - How Independent Security Researchers work with Medical Device Manufacturers: The Bad, The Ugly & The Great (BUG)


uk: Комп'ютерні науки

description: The Do-It-Yourself Biology (DIYBio) community arose starting in the mid-2000's. This community falls is typically described in two ways in public discourse. More conservative elements paint this community as a cause of concern - increased access to life sciences technology, knowledge, and capabilities raises concerns that community members may produce biological products for harmful purposes. More progressive elements highlight that the self-enforcing nature of the community mitigates harmful outcomes, and that the DIYBio community can also contribute to society by addressing gaps in science education, innovation, and workforce training. This presentation will be a distillation of work I have been doing during my PhD work to build a better understanding of community labs - one of several significant segments of the DIYBio community. I will provide a risk assessment framework that national security experts appear to use in assessing risks from emerging technologies and related phenomena. I will then describe how this risk assessment framework may interpret DIYBio as an emerging threat to national security. The remainder of the presentation will be on presenting the nuances that complicate this allegedly straightforward assessment.

uk: вхід без авторизації

name: Biohacking Village - You Know what They Say When You Assume...


uk: Комп'ютерні науки

description: This panel features five industry folks working toward improving medical device security through multiple channels and methods and discussing how provider, vendor, and security collaborations are leading toward significant improvements in medical device security. These panelists and moderator represent the organizations working to actively collaborate between the independent researchers, device vendors, information security officers, clinical engineering, electronic medical records vendors, and security companies with a goal of continual improvement. These 5 represent part of a significantly larger effort, and have contributed to open standards.

uk: вхід без авторизації

name: Biohacking Village - Advancing Medical Device Security


uk: Комп'ютерні науки

description: Russia has seized upon the global Covid-19 pandemic as an opportunity to use its multifaceted, multidirectional information and cyberwarfare strategy against its prospective enemies. Russian state-sponsored hackers are using the coronavirus to spread different types of malware against western nations. Universities, hospitals and scientific facilities with access to classified information are targeted to steal data and research related to Covid-19. In this respect, the presentation will cover cyber-attack efforts against the healthcare industry in the United States and Europe. The presentation will discuss tactics, techniques and procedures (TTPs) of the advanced persistent threat (APT) groups. It will review Russian ransomware criminal actors and their communication channels (dark web). The presentation also will highlight the role of the siloviki (people of power) in the country’s information warfare efforts. It will examine the structural units of the Russian Federal Security service (FSB) and Russian military intelligence (GRU), their projects and their networks of allied criminal groups. It will discuss how intelligence organizations are using APT groups through complex outsourcing strategies to conduct cyberwarfare over a broad spectrum. The presentation suggests several factors that drive the current trends in Russian cyber capabilities. It will also survey current trends: growing sophistication of TTPs and tools, supply chain threats, false flag operations, deception tactics, third party entry vectors and cyber espionage. Finally, the Russian Cyber Threat presentation will cover the role and mission of the Russian Foundation for Advanced Research Projects in the Defense Industry, the Russian Army’s Technopolis, and Russia’s chemical, biological, medical, informational and research efforts.

uk: вхід без авторизації

name: Biohacking Village - Russian Cyber Threats in The Pandemic Era


uk: Комп'ютерні науки

description: The modern diabetic uses wirelessly connected devices to manage their insulin levels at the cost of an increased attack surface. In our talk, we introduce DE, US cybersecurity approaches. We hack an insulin pump. And, we determine the impact of remediation on the DIY community."

uk: вхід без авторизації

name: Biohacking Village - DIY Diabetics and a Million Boluses: Global Response and Impact to the Diabetics Community


uk: Комп'ютерні науки

description: Evangelists for improved security in healthcare have long been concerned about vulnerabilities and impacts stretching beyond privacy and personal health information into the disruption of care and worsening of patient outcomes. As the healthcare system struggles under the burden of the COVID-19 crisis, are there parallels between pandemic preparedness and response and the aims and objectives of healthcare security? Join quaddi and r3plicant, hackers who have been moonlighting as practicing physicians caring for COVID patients, as they discuss what recent experiences and events have taught them about how to reframe and re-address security challenges with the hard-earned hindsight and wisdom of medicine's collective struggles.

uk: вхід без авторизації

name: Biohacking Village - Lessons Learned From the Pale Horse: What Covid- 19 Can Teach Us About Healthcare Cybersecurity


keywords:
History of Science
Lecture
Teaching Aid
Shadow Projection
Media History
Experimental Physics
Material Culture

de: Physik, Astronomie

fi: fysiikka, tähtitiede

uk: Фізика, Астрономія

en: Physics, Astronomy

fr: Physique, Astronomie

da: Fysik, Astronomi

es: Física, Astronomía

nl: Natuurkunde, Astronomie

uk: Математика, природничі науки

description: The documentary film gives an insight into the teaching concept of Göttingen physicist Robert Wichard Pohl (1884-1976). It's focus is on the use of Pohl ideas and apparatuses in modern basic lectures on experimental physics in Göttingen. The film depicts the work of the lecturers and lecture hall technicians which is complemented with interviews. It was realized within of the research project "Projektionen. Die Lehrsammlung Robert Wichard Pohl" (2020-2022, funded by the program PRO*Niedersachsen).

uk: вхід без авторизації

name: Behind the Shadows: Lectures on Experimental Physics in the Tradition of Göttingen Physicist Robert Wichard Pohl


uk: Комп'ютерні науки

description: In a Q/A format, with plenty of time for audience questions, Ms. Savage will explain the basic privacy legal landscape, what the hot debate topics are as people seek to change those laws nationally, and ways to influence the debate. At last count (June 4) there were three Covid-specific federal legislative proposal to oversee how commercial/ad-tech companies keep private the information they collect to help track Covid. There are approximately another 10 bills that propose to generally revamp ad-tech privacy on a national basis.

uk: вхід без авторизації

name: Biohacking Village - What's Up with Proposed Privacy Legislation and how to influence the debate


uk: Комп'ютерні науки

description: 2020 has been the year of COVID-19. The healthcare sector has been on the frontlines of battling this pandemic. There was significant projected demand for rapidly-manufactured ventilators during the early stages of the COVID-19 pandemic in the United States. Massachusetts was hard hit during the early stages of this pandemic, and the state’s largest healthcare delivery organization brought together the open source community to develop new technologies and processes for rapidly developing resources needed to treat predicted growth of infections. The open source community came together to develop rapid prototype ventilators that could be potentially mass produced in quick succession. Many of these devices did not have built-in monitoring capabilities, so there was an anticipated need for staff to adequately track alarms in a centralized manner for these devices. The Open Ventilator Monitoring Project addressed this need by rapidly creating a system that allows hospitals to monitor alarms and patient data from ventilators, integrating the status of multiple devices into a single display, similar to a central nursing station. During the design process of this project, an additional need was brought to the team’s attention. Due to infection control procedures that require closing doors to patient rooms, clinical staff were unable to hear alarms from ventilators that were not already integrated into a traditional central monitoring system. The team then pivoted to develop a solution to modify the hardware and software system to include the ability to auditorily monitor and alert based on the sound pressure of these ventilator alarms. To date, the team has delivered a Minimum Viable Product (MVP), which has undergone limited lab testing in the Massachusetts General Hospital’s Medical Device Interoperability and Cybersecurity Program Lab (MGH MD PnP). The project has longer term goals of safety/integration, and ultimately, deployment within settings such as field hospitals. It is expected that this project's capabilities may be useful to many hospitals, extending beyond the constantly-changing emergency of COVID-19's spread. While the software has been designed to monitor ventilators, the project's architecture - utilizing APIs and plugins - is extensible to other network environments and other device types. Ultimately, hospitals in the U.S. have not experienced a shortage of traditional ventilators, and so our software was not needed during the Covid-19 crisis. However, we present a framework for rapidly developing software in crisis situations along with a set of lessons learned for those who follow in future crises.

uk: вхід без авторизації

name: Biohacking Village - Open Remote Ventilator Monitoring Project


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Biohacking Village - The Underestimated Threat Vector: Homogeneity


uk: Комп'ютерні науки

description: Building on conversation within the Biohacking Village at DEFCON 27, and expertise in clinical care and implementation science (Dameff, Doerr, Tully), cybersecurity in healthcare (Coravos, Dameff, Tully), device policy and regulation (Coravos, Doerr), and informed consent (Doerr), we have defined a framework for “cybersecurity informed consent,”(CIC) a platform we hope will help directly address the patient (and clinician) awareness gap of the cybersecurity vulnerabilities of connected devices, enhancing the ecosystem of trust.(Tully, et al., 2020) In February 2020, we convened a 30-person advisory team comprised of white hat hackers, clinicians, and device makers focused on identifying potentially appropriate clinical scenarios for a demonstration of CIC, informed by legal and policy research performed by Science & Society Certificate Capstone students from Duke University (phase 2). We will present an overview of this work for comment and discussion as we move into the third phase of our project: implementation and assessment of CIC within the clinic.

uk: вхід без авторизації

name: Biohacking Village - Cybersecurity Informed Consent 4 Medical Devices


uk: Комп'ютерні науки

description: Molecular tagging is an approach to labeling physical objects using DNA or other molecules that can be used in cases where methods like RFID tags and QR codes are not suitable. No molecular tagging method exists that is inexpensive, fast and reliable to decode, and usable outside a lab setting to create or read tags. To address this, we present Porcupine, an end-user molecular tagging system that features DNA-based tags readable within seconds using a portable nanopore device. Porcupine's digital bits are represented by the presence or absence of distinct, nanopore-orthogonal DNA strands, which we call molecular bits (molbits). We classify molbits directly from the raw nanopore signal, avoiding basecalling. To extend the tag's shelf life, decrease readout time, and make tags robust to environmental contamination, molbits are prepared for readout during tag assembly and can be stabilized by dehydration. The result is an extensible, real time, high accuracy tagging system that includes a novel approach to developing nanopore-orthogonal barcodes.

uk: вхід без авторизації

name: Biohacking Village - Porcupine: Rapid and robust tagging of physical objects using DNA with highly separable nanopore signatures


uk: Комп'ютерні науки

description: Digital technologies are the future of medicine--and perhaps also public health--but these innovative tools that offer great promise for higher quality, more affordable, more accessible care also pose new risks to patients. Using real-world examples, this presentation will make the case for expanding the list of harms considered when determining the risk-benefit profile of a medical product in the digital era of health. We will consider security practices -- and sometimes their absence -- and disparities in both access to technologies and technical literacy. Digital technologies - and in particular remote monitoring technologies such as wearables and other in - home smart sensors have the potential to transform health, healthcare, and health research. But these innovative tools also pose new risks to patients. Risk-benefit analysis is the bedrock of clinical decision making, from formulating individual treatment plans to drug approval decisions. However, while shaky data rights in the United States put patients at risk when they use digital health products, these risks are poorly understood and rarely included in risk-benefit analyses. This presentation will illustrate the new risks to patients posed by their digital health footprint-from challenges accessing health care to discrimination in the workplace-and explain for readers why data rights and security must be folded into a contemporary definition of 'patient safety'.

uk: вхід без авторизації

name: Biohacking Village - Redefining Patient Safety in the digital era


Biohacking Village - Fireside Chat

uk: Комп'ютерні науки

description: Discussions around: - Intersection of big data and patient rights - Real World Data and how to best serve patients in the digital era - Cybersecurity risk for medical devices - How FDA is working with security researchers (e.g., the FDA-led Evidence Accelerator at the FDA)

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Institutional Review Boards (IRBs) are groups that examine research plans of fellow members by applying community standards to that research. IRBs help researchers consider rigorous methodology, ethics and safety and the protection of vulnerable populations of people or animals. IRB approval is required by the FDA before human clinical trials can begin. People who serve on IRBs include general community members, researchers, bioethicists, physicians, clinicians, lawyers and members of regulatory agencies. Traditional research corporations and universities have internal IRBs, but external independent IRBs do also already exist. However, it is usually very expensive to hire an independent IRB, so most non-traditional scientists cannot afford it. This creates a major hindrance to bringing innovative human health related solutions to the general public. There has recently been a small grassroots push in the biohacking community to try to create an independent IRB for the biohacking community to help bridge this gap.

uk: вхід без авторизації

name: Biohacking Village - Towards an Institutional Review Board (IRB) for Biohackers


EuroPython 2020 - Closing Session

uk: Комп'ютерні науки

description: "Closing Session EuroPython 2020 - - 2020-07-24 - Microsoft Online License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons...

uk: вхід без авторизації


EuroPython 2020 - Lightning Talks 07/24

uk: Комп'ютерні науки

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description: In spite of the dynamic nature of their favorite language, some Python developer have a huge desire to statically analyse it. This can indeed be useful for linters, type inference, auto-completion and all the tooling some developers expect from modern IDE. We all know that lazy binding prevents even the simplest function call or attribute lookup to be reliably analyzed. Yet we try. And Python as this fabulous ``ast`` module, saving us from writing a parser! Isn't that a strong incentive to do static analysis? This talk presents two modules developers can build upon to build such analyzers: - gast, a generalization of the Python AST that provides a common API for all the variant of the Python AST, from python 2.7 to Python 3.8 - beniget, an analyzer of the Python AST (built upon gast) that provides a useful and well-known abstraction to understand programs: use-def chains Built upon these two modules, memestra is a static analyzer of deprecated function calls, developed in partnership with QuantStack. A tool which, given a module, reports any use of deprecated APIs. Let's explore how such a module can be built and unveil the mysteries of static analysis.

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name: Tooling for Static Analysis of Python Programs


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description: - Overview - The history of PyCon JP has been 10 years since 2010 - The conference itself has started up → expanded in scale → stabilized - At the same time, we are working on spreading Python throughout Japan. - And what to do in the future - History of PyCon JP - The origin of PyCon JP was PyCon APAC 2010 in Singapore - We have been holding PyCon JP every year since 2011 - Our first PyCon was PyCon mini JP in 2011 - We invited PyCon APAC from Singapore in 2013 - Participants are increasing steadily - There is English tracks and Japanese talks - 2013: Association - Purpose: We have established a legal entity to make it easier to manage money and contracts for PyCon JP event - I have been a director since 2013 - 2014-1016: Chair od PyCon JP event - I was the chair of PyCon JP event from 2014 to 2016 - Purpose: Open management - I opened every staff meeting to make it easier for new staff to join - We created an operation manual and made it reusable - We regularly publish our activities on the blog to get interested in event management - 2015-: PyCon mini - Small PyCons in the region were held in Sapporo and Hiroshima in 2015 - It has been held in various places such as Kumamoto and Osaka - PyCon Kyushu is held all over Kyushu(Fukuoka -> Okinawa -> Kumamoto) - We have been sharing operating know-how and supporting money and people - We have also provided a subdomain of pycon.jp - 2016-: Python Boot Camp - Objective: We visit all over Japan to teach Python → We connect with local people → We hope local communities to start up - We teach an introduction to Python programming in half a day - We hold 40 times in 3.5 years with 950 local participants - We hold in cooperation with local staff and local teachig assistants - Result: Some local communities have been created - PyCon JP has more people coming from local area - 2019: Python Boot Camp Caravan, Regional Meetup - Purpose: To strengthen the connection between local communities - A regional meetup was held at PyCon JP 2019 with local community members - About 30 people gathered from all over Japan for this event - We had various discussions and shared ideas and worries - We hold local meetups online - We hold the first online meeting using Zoom, with 15 participants from all over Japan - Future - We want to hold PyCon JP outside of Tokyo - We want to conquer all prefectures at Python Boot Camp - We want PyCon mini to be held all over Japan - Summary - We have spread Python throughout Japan with various activities - I think it's important to meet - First we go to meet local people - Reunion strengthens relationship

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name: Decade of PyCon JP: How we spread the Python community in Japan


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description: due to COVID-19(Coronavirus disease 2019), we changed our life, social, and our community. most of us are engineer/organizer/designer/contributor with passion to communicate to everyone who has similar background. in COVID-19 disease, we are forced to reduce social event for keeping safe. in case from China/Korea/Malaysia.... we can see the number is decreasing and be controlled after each government announced new policy: Face masked, Social Distancing, Quarantine, and Isolation. it meant we can not go to some place to hangout like usual, of course we have a lot of SMS tools/platform/media to keep relationship with our love, but for community, we easily lost our passion. and now, I want to share some doing/status of communities in Asia or other region I knew during/after COVID-19.

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name: Community-oriented conference status during COVID-19: what can we do during/after COVID-19


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description: Identified in December 2019, the novel Coronavirus has infected 2.7 million worldwide, and claimed the lives of 0.2 million. Amidst this deadly pandemic, I started my open source project, Corona-Net, in the hopes of contributing to the global fight against the Coronavirus. Corona-Net is a 3-part project dedicated to the classification, binary segmentation and multi-class segmentation of COVID-19. I first leverage the EfficientNet model for COVID-19 diagnosis, then utilise and refine the U-Net architecture for both binary and 3-class (ground-glass, consolidation, pleural effusion) segmentation of COVID-19 symptoms, through inference on the COVID-19 CT segmentation (chest axial CT) dataset. Through Corona-Net, I aim to develop a reliable, visual-semantically balanced method for automatic COVID-19 diagnosis, as well as extend an invitation to all to collaborate and stand together against this pandemic.

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name: Corona-Net: Fighting COVID-19 with Machine Learning


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description: Undoubtedly this will be the decade of low-cost and high-performance cloud IoT core development. All cloud IoT solutions will have to meet the same two fundamental requirements: Performance and Data Integrity. Having said that, AsyncIO comes to mind. While researching similar topics on cloud IoT solutions, we noticed that the vast majority of such solutions primarily includes performance. In this discussion we would like to demonstrate how you can ensure that a low-cost, fast developed cloud IoT core can ensure that its data is not corrupted and is accurate for the user. Technically speaking, it is very interesting to analyze how a bucket of async/await tasks can handle (consume or even produce) messages from or to message queues, interact with other (even non pythonic) modules inside the core, manage socket connections and many other functionalities that make Python3.6+ the heart of any IoT core. Note that it is equally important to check imported data with periodic tasks, acquire statistics from devices and sanitize database data in order to ensure data integrity. Our goal: Firstly, to encourage new developers to get involved with cloud IoT cores and use Python and AsyncIO as the heart of their core. Secondly, as new developers we would like to show our logic and our prefered python libraries and techniques in order to receive feedback from more experienced developers. We are a team of software engineers located in Athens, Greece working for a fast growing startup in San Francisco (https://www.veturilo.io). We have developed our own cloud IoT core for a vehicle fleet management SaaS.

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name: Ensuring data integrity with asynchronous programming in a cloud IoT core: Python3.6, AsyncIO, Flask, RabbitMQ. A simple and powerful combination for IoT data integrity.


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description: What do we do when the only test requirements we have are "the new system should have the exact same results as the old system"? Golden Master Testing may help. The idea behind golden master testing, also called characterization testing, is quite simple: We don't write test cases that specify what the expected result is. Instead, we take the output from a prior, working version of a program (the golden master) and compare it to the result of the current version. If there is a difference between the output of the current system and the golden master, we may have found a bug. If implemented right, Golden Master testing can be very useful to test legacy systems or data processing programs with complex input and outputs. Golden Master Testing is also a good addition to our regression testing processes. In practice, implementing Golden Master testing is not quite that easy. Just checking if the outputs are equal will often not work: If the output includes times and dates or random elements, a simple comparison will not be enough. Luckily for use, with Python, we have the perfect tool to process all kinds of outputs and only look at the parts of the output that are important for the outcome of the test. In this talk, we will look at best practices for Golden Master Testing with Python. We will see techniques to identify and quantify the relevant differences between our golden master and the current output.

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name: Tests that (Almost) Write Themselves: Hints for Golden Master Testing in Python


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description: In my work as a consultant, coach, and organizational leader- I work with amazing companies around the world, helping them craft and implement strategies to build high-performing teams. And when I work with these teams, I notice that there are two factors that make all the difference between a good organization and one that is changing the world. The first is having a culture of collaboration – and this means collaboration across individuals, across departments, and across teams – really - it’s a culture that’s less about competition and cutthroat political systems.. And more about helping each other achieve the extraordinary. The second quality is organizations that treat and expect their employees to act like owners. Owning not just their behaviors, actions, and responsibilities, but owning outcomes and becoming a voice for the organization. No matter where your organization is at today, the good news is– there are proven strategies to create a culture of collaborative ownership at your organization and that’s what I’m going to teach you in this course. I’ll share how you can build momentum from a carefully crafted vision and goals, I’ll help you improve communication and drive ownership, I’ll help you foster creativity, and finally, I’ll help you train up leaders in your organization and help you meet more of your goals. So, if you’re ready to transform your organization. let’s go! LEARNING OBJECTIVES: Attendees will learn: How to transform a command-obey dynamic to one of creativity and innovation through opportunities for ownership. How to collaborate with opportunities for ownership through proven methods to increase connectivity and engagement in the team. How to foster captainship within your organization. How to apply the ideas of collaborative ownership specifically in individual organizations. SESSION OUTLINE: Fostering an Ownership Culture (Creativity and Innovation)- Ownership Activity -Fostering a Collaborative Culture- Collaborative Activity - Fostering Captainship Culture - Captainship activity- The Application of Collaborative Ownership for Personal and Organizational Success- Applying Collaborative Ownership to Organizations and Self Activity -Conclusion Q&A

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name: Cultivating a Culture of Creativity, Collaboration, and Captainship


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description: The Corona Virus - COVID-19 pandemic caused a significant impact on public services. In particular, it brought a massive digitalization of services that were offered in person. In addition, there was a great deal of pressure on official channels, whether to seek a service or to report a fact. Qualified and official information becomes an element in combating the pandemic. Knowing where to look for information, reporting a fact or getting a qualified response are essential steps to maximize the efficiency of the citizen's decision-making process, especially in a scenario of such uncertainty. The State has a fundamental role, whether as an inducer, executor or provider of the means necessary for information and decision-making to be effective at that time. Connected by the growing demand, the State of Goiás, through the State's Comptroller General, published a series of technological tools with the objective of bringing information to the citizen, promoting social control and improving public policies to fight the Coronavirus. This document seeks to show how technological tools were adopted and implemented in terms of transforming the ombudsman channels in the State of Goiás through the State's Comptroller General in actions to combat the Coronavirus pandemic. The document seeks to show how the transformation of the ombudsman channels occurred through machine learning techniques and data visualization.

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name: Digital Transformation in the fight against Coronavirus: COVID-19: An experience of the Comptroller General of the State of Goiás


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description: GeoDataframes are awesome for working with GIS data in Python as it provides the goodness of pandas for geographic data but So let's use them to create some cool interactive maps This talk introduces Geopatra, an open-source mapping library that can be used to quickly map geodataframes with libraries like folium, kepler.gl, plotly, etc. Currently interactive mapping with geodataframes is not very straighforward for beginners and requires you to understand the interfaces exposed by different mapping libraries. geopatra attempts to abstract all the complexities and tries to provide a unified framework for mapping geodataframes 1. Introduction (2 mins) * Who am I? * Setting the expectations 2. Working with Geodataframes (5 mins) * Reading Spatial data as a GeoDataframe * The awesomeness of GeoDataframes (An overview of some of the amazing things you can do with a single line of code eg: Spatial joins, Manipulations, and transformations) * Now that we have established our love for geopandas lets map geodataframes 3. Let the Mapmaking begin (10 mins) * Basic Non Interactive maps with Geopandas and Matplotlib * Creating basic interactive maps with Folium, Plotly & Kepler (GeoJSONPlots, Clustermaps, Heatmaps, H3gridmaps) * The number of steps vs the number of necessary steps 4. Enter Geopatra (10 mins) * The need for Geopatra (Could it be any Easier and Faster) * Wrapping up popular mapmaking libraries for GeoDataframes * Create interactive maps with just a single line * Future work and Discussion :)

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name: Interactive Mapmaking with Python: Introducing Geopatra for quickly creating amazing interactive maps with dataframes


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description: As the impact of software increasingly reaches farther and wider, our professional responsibility as developers becomes more critical to society. The production systems we design, build and maintain often bring inherent adversities with complex technical, societal and even ethical challenges. The skillsets required to tackle these challenges require us to go beyond the algorithms, and require cross-functional collaboration that often goes beyond a single developer. In this talk we introduce intuitive and practical insights from a few of the core ethics themes in software including Privacy, Equity, Trust and Transparency. We cover their importance, the growing societal challenges, and how organisations such as The Institute for Ethical AI, The Linux Foundation, the Association for Computer Machinery, NumFocus, the IEEE and the Python Software Foundation are contributing to these critical themes through standards, policy advise and open source software initiatives. We finally will wrap up the talk with practical steps that any individual can take to get involved and contribute to some of these great open initiatives, and contribute to these critical ongoing discussions.

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name: Meditations on First Deployment: A Practical Guide to Responsible Development: Keynote


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description: Many of us are using Kubernetes in production. A Kubernetes Operator is a way to automate packaging, deploying, and managing of a Kubernetes Application. It is a software alternative to a human operator who has deep knowledge of how to set up, deploy, and manage a particular piece of infrastructure and what to do if it isn’t behaving correctly. Let’s see how we can automate all of this while staying in the Python ecosystem. It will be helpful to know some basic concepts of Kubernetes(Deployments, Services, Pods, Configmap etc.) and Celery(docs.celeryproject.org) to get the most out of this talk. Talk is divided into four phases. Phase I - Problems and Opportunities We're going to see some simple examples/problems where a lot of manual effort is involved so as to connect audience to the problem. We're going to discuss problems with configuration management, database cluster setup and introduce the focus problem of the talk which is around automating the setup of a Celery cluster. Phase II - Incrementally Approaching the Solution We're going to incrementally approach the automation each of the manual steps involved in running a Celery cluster in Production. We're going to discuss the extension capabilities in Kubernetes using CRDs and Custom Controllers which are going to help us manage our Celery cluster automagically. Phase III - Celery Operator in action We're going to see the code of custom controller and the whole operator in action. We create the newly defined celery resource and see how the operator works on bringing up the worker and flower deployments and handles autoscaling based on queue length. Phase IV - Conclusion and Q&A We're going to talk about different use-cases and what is world doing with Operators. We'll discuss the next steps for the Celery operator and some resources to help build operators. We'll end the talk with a Q&A.

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name: Advanced Infrastructure Management in Kubernetes using Python: Automate managing complex applications in a cloud native way using Operators written in Python


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description: We had been planning the EuroPython 2020 conference in Dublin, Ireland ever since late in 2019, when we ran the CFP. In March, it became apparent that the COVID-19 crisis would not be over by July 2020 and so we had to make a hard decision to either drop the event for 2020 or go online. Since we wanted to support the community even in difficult times, we decided to go for the online version -- even though this would be much less engaging than the in-person event. For the past months, we have been creating a concept for the conference from scratch. The talk will present our approach, how it came to be and which challenges we faced. The concept should be usable by other conference as well.

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name: Running EuroPython 2020 as an online conference: Challenges we faced switching to an online event due to COVID-19


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description: This will be a technical talk where I'll explain the inner workings of the machine learning algorithms inside of Rasa. In particular I'll talk about why the transformer has become a part in many of our algorithms and has replaced RNNs. These include use-cases in natural language processing but also in dialogue handling. You'll see a live demo of a typical error that an LSTM would make but a transformer wouldn't. The algorithms are explained with calm diagrams and very little maths.

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name: Why Transformers Work: And RNNs Fall Short


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description: I will review the main mechanims for memory allocation and how the garbage collector works in conjunction with the memory manager for reference counting of the python objects.Finally, I will comment the best practices for memory managment such as writing efficient code. These could be the main talking points: -Introduccition to memory management -Garbage collector and reference counting with python -Review the gc module for configuring the python garbage collector -Best practices for memory managment

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name: Python Memory Management 101: Deeping in Garbage collector


Find a new job - Sponsor Presentations

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description: "Find a new job - Sponsor Presentations EuroPython 2020 - Talk - 2020-07-24 - Ni Online Many of our sponsor are looking to hire more developers and EuroPytho...

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description: We need help with organizing and running EuroPython 2021. In this session, we will explain how the EuroPython workgroup model works and where you could help.

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name: EuroPython 2021: Help us build the next edition!


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description: During the pandemic lockdown of COVID-19, we found a very different context from the usual: a) students with much more time available for learning b) many students who did not have a personal computer and could only access classes by cell phone c) difficulty to realistically assess learning. In this lecture we will present the real experiences in a traditional programming course given during the Covid-19 pandemic.

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name: Python Emergency Remote Teaching: Online Python programming course offered in response to a COVID-19 pandemic lockdown


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description: I will share on how to utilize serverless architecture for a less common scenario - unit testing. As part of the talk, we will also discuss different approaches to parallelizing unit test suite execution. Attendees will also learns on cost-benefit analysis related to increasing developer productivity. Outline: - Introduction - Different approaches to parallelizing unit test execution with pro & cons - What we learned (gotcha) when implementing serverless as a unit test runner - Cost-Benefit Analysis and usage report, - Q&A

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name: Running Unit Test on Top of Serverless Service: Increase your parallelization by 50x


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description: As software engineers work on CI/CD process as soon as they start a new project, data scientists and ML engineers define a pipeline for data as it flows through a typical workflow. Each step of the pipeline is fed data processed from its preceding step as CI/CD process starts from code changes. "Pipelining ML project" is sometimes misleading as it implies a large project with a group of engineers working on some large systems , being considered to be hard for an individual and unnecessary for a small project. Regardless of its size, having well organized pipelines for any ML projects is essential to succeed and actually it could be done easily with utilizing a proper tool. In this talk, we will go through a machine learning workflow divided into a few steps composing a ML pipeline from data ingestion to model deployment. Each step depends on data produced by previous step, which are controlled by DVC. DVC is open-source version control system for data scientist and ML engineer helping them to organize data, models and experiments for some ML projects. The presentation will not only introduce how to use the tool but also show how to organize a ML pipeline with some examples. The goal of this talk is to motivate data scientists and ML engineer to start building machine learning pipeline with DVC. Audience might expect a guide to using DVC for automating the pipeline. Also I will give some explanation about concepts of machine learning related techniques necessary for understanding the pipeline. This session is designed to be accessible to everyone in beginners level. Understandings of basic concepts of machine learning and version control system (preferably, Git) might be helpful but not mandatory for the audience.

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name: Automating machine learning workflow with DVC: What data scientist / ML engineer wants to do while software engineers are busy with CI/CD.


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description: Data cleaning is one of the most important tasks in data science but it is unglamorous, underappreciated and under-discussed. These are some common tasks involved in data cleaning but not limited to: - Merging/ appending - Checking completeness of data - Checking of valid values - De-duplication - Handling of missing values - Recoding Most, if not all, of the time, the datasets that we have to analyze are unclean. i.e. they are not necessarily complete/ accurate/ valid. This will impact the accuracy of our analysis if we do not clean them properly. This talk covers how to perform data cleaning and understanding using primarily Pandas and Numpy. If you’re new to data analytics/ data science and are interested how to use Python to perform analysis, or if you're an Excel user hoping to move to Python, this talk might be for you. Participants should be at least familiar with the basics of Python programming.

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name: Top 15 Python Tips for Data Cleaning/ Understanding: With two bonus tips!


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description: A lot of advancements are happening in the field of Deep Learning and Generative Adversarial Networks are one of them. We have seen GANs being applied for photo editing and in-painting, generating new image datasets and realistic photographs, increasing resolution of images (Super Resolution), and many more things. Some people have also exploited GANs for generating fake content. All the above-mentioned examples are result of a technique where the focus is to generate uncommon yet original samples from scratch. However, these examples have very less commercial applications and GANs are capable of doing much more. The focus of this talk is a technique called "Neural Style Transfer (NST)" which has numerous commercial applications in the gaming world, fashion/design industry, mobile applications, and many more fields. Challenges and technicalities of NSTs will be covered in great detail. We will teach the machines on how to paint images and utilize Style Transfer networks to generate artistic artefacts. The flow of the talk will be as follows: ~ Self Introduction [1 minute] ~ A Succinct Prelude to GANs [10 minutes] ~ Understanding Style Transfer [5 minutes] ~ Learning about Neural Style Transfer Networks [5 minutes] ~ Loss Functions: Content, Style, Total Variantion [10 minutes] ~ Code Walkthrough and Result Analysis [5 minutes] ~ Challenges and Applications [5 minutes] ~ Questions and Answers Session [3-4 minutes]

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name: Painting with GANs: Challenges and Technicalities of Neural Style Transfer: Building Artistic Artefacts using Generative Networks


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description: The talk will showcase how the Python community started and took off in Bloomberg, and how it can serve as a model that can be used within other companies as well. This includes details on how Bloomberg's Python community got started and grew to the place that it is today, as well as tips from its leaders personal experiences.

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name: Growing a Python Community at an Enterprise Scale


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description: Python is the lingua franca for data analytics and machine learning. Its superior productivity makes it the preferred tool for prototyping. However, traditional Python packages are not necessarily designed to provide high performance and scalability for large datasets. From this talk you will learn how to get close-to-native performance with Intel-optimized packages, such as numpy, scipy, and scikit-learn. The next part of the talk is focused on getting high performance and scalability from multi-cores on a single machine to large clusters of workstations. It will be demonstrated that with Python it is possible to achieve the same performance and scalability as with hand-tuned C++/MPI code: - Scalable Dataframe Compiler (SDC) makes possible to efficiently load and process huge datasets using pandas/Python. - A convenient Python API to data analytics and machine learning primitives (daal4py). While its interface is scikit-learn-like, its MPI-based engine allows to scale machine learning algorithms to bare-metal cluster performance. - From the talk you will learn how to use SDC and daal4py together to build an end-to-end analytics pipeline that scales to clusters, requiring only minimal code changes.

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name: The Painless Route in Python to Fast and Scalable Machine Learning


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description: Ever wanted to try out NLP methods but it felt it too cumbersome to set up a workflow for textual data? How to enrich your data based on textual features and explore the results? NLPeasy (https://github.com/d-one/NLPeasy) does that: Enrich the data using well-known pre-trained models (Word embeddings, Sentiment Analysics, POS, Dependency Parsing). Then start the Elastic Stack on your Docker. Set-up indices and ingest it in bulk. And finally generate Kibana dashboards to explore the results. Complicated? Not at all! Just do it in a simple Jupyter Notebook. In this presentation we will give an architecture overview of the different components and demonstrate the capabilities of this Python package.

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name: NLPeasy - a Workflow to Analyse, Enrich, and Explore Textual Data: Use pre-trained NLP-models, ingest into Elastic Search, and enjoy auto-generated Kibana dashboards!


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description: Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications from a laptop to a cluster. While broadly applicable, it was developed to solve the unique performance challenges of ML/AI systems, such as the heterogeneous task scheduling and state management required for hyperparameter tuning and model training, running simulations when training reinforcement learning (RL) models, and model serving. Ray is now used in many production deployments. I'll explain the problems that Ray solves for cluster-wide scaling of general Python applications and for specific examples, like RL workloads. Ray’s features include rapid scheduling and execution of “tasks” and management of distributed state, such as model parameters during training. I'll compare Ray to other libraries for distributed Python. This talk is for you if you need to scale your Python applications to a cluster and you want a robust, yet easy-to-use API to do it. You don't need to be a distributed systems expert to use Ray. You'll learn when to use Ray versus alternatives, how it’s used in several open source systems, and how to use it in your projects.

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name: Ray: Scalability from a Laptop to a cluster: Scale your applications from a laptop to a cluster with ease


Machine Learning for Everyone

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description: Machine learning (ML) is becoming an essential technology for our day to day life. Stop taking ML as a threat and learn it today as not learning it is a HUGE LOSS! Get started today with ML in Aaron's remarkable 45-mins talk. We will begin by talking about the paradigm of ML, then taking a deep dive into Neural Networks and building a Neural Network from scratch with Keras and TensorFlow (the hottest machine learning framework). You'll master the magic of neural networks that are powering incredible advances both in AI, self-driving cars, and much more! Finally, we will finish off by talking about Reinforcement learning and how it is empowering YouTube suggestions along with tips-and-tricks from a specialist plus a grand finale mind-blowing demo. Ready to master the paradigm of ML? Let's get started.

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description: GoJek has millions of monthly active users in Indonesia across our 20+ products and services. A major problem we faced was targeting these customers with promos and vouchers that were relevant to them. We developed a generalized model that takes into account the transaction history of users and gives a ranked list of our services that they are most likely to use next. From here on, we are able to determine the vouchers that we can target these customers with. In this poster, I will be presenting our process while developing the model, the challenges we faced during the time, how we used PySpark to tackle these challenges and the impact it had on our conversion rates.

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name: Developing a match-making algorithm between customers and Go-Jek products!


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description: As Deep Learning Engineer and Researcher we are always trying to optimize some bottleneck computation in our programs. Sometimes we are faced with situations when scientific libraries like NumPy, SciPy aren't just cutting it or worse there are no libraries that implement the esoteric function on our expensive GPU hardware. *Writing Custom C and Cuda Extension becomes an important skill and necessity for applications that require really fast computation. * In this talk, we go through a detailed example of image search on billions of items, we write custom C and Cuda kernel for distance computation and learn how to connect them seamlessly with our python codebase. We compare methods for writing these extensions and bindings for python in terms of both speed and ease of use.

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name: Writing Extensions and Bindings for GPU made Easy: Writing Bindings for C and CUDA code and Packaging it with setup.py in 30 min or less


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description: Discrete event simulation is a very powerful technique in many fields, like transport, warehousing, hospitals, manufacturing and logistics. There are several commercial packages that provide high end GUIs to design a system and to animate the operations. These packages are neary always close source and are very expensive (think USD 10-20 K per user). Often implementing complex algorithms is cumbersome. On the other end of the spectrum are open source packages that use existing program languages. Python is particularly interesting because of the vast number of (scientific) packages, like machine learning, statistics and databases. In te Python landscape there is SimPy, that is rather limited in functionality. In this poster I will present my discrete even simulation package called salabim. Salabim uses coroutines via generators and makes it very easy to model complex systems. The package offers advanced real time (2D) animation, monitoring and statistical sampling. I will show some applications and how a simple model is built up. Ruud van der Ham, core developer salabim

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name: Simulation of logistic systems in Python with salabim: Salabim can help to model complex logistic systems, like warehouses, ports, supply chains, hospitals


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description: Describe the process I went though to return to and finish the code I wrote for my PhD modelling the geographic growth and decline of a Bulletin Board Systems (BBS) community called FidoNet. I had to leave the project for 2 major surgeries and finally returned to finish, validate the results and present a paper at the Sunbelt conference in Paris.

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name: Resurrecting a django project in python 2.7 for 3.8: Pitfalls and euphoria updating my GeoDjango 1.11 PhD project to Django 3.0


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description: HTTP is the foundation of the current web, and HTTP/3 is the upcoming major version of it. The new version is built on top of the QUIC transport protocol, originally developed in Google. HTTP/3 can change the Internet as we know it today. Since its beginning in the 90s, HTTP transfers data over TCP to ensure reliable connections between clients and servers. QUIC is a TCP alternative, reimplemented on top of unreliable and connectionless UDP. Proprietary Google implementation of QUIC is deployed worldwide and supported by Chrome browsers. Future HTTP/3 will be hopefully standardized by IETF soon, but many diverse implementations are available already today. HTTP/3 improves performance and increases privacy. The switch from TCP to QUIC allows us to address the inherent limitations of previous HTTP versions. The QUIC protocol is completely encrypted, including traffic control headers, which are visible in TCP. This talk introduces HTTP/3 and the underlaying QUIC protocol. It shows both advantages and disadvantages of the new technology, and it describes the landscape of the current implementations and suggests what you can try today.

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name: HTTP/3 – Why should I care?: Internet foundations reworked.


EuroPython 2020 - Lightning Talks 07/23

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uk: Комп'ютерні науки

description: In this talk I am going to present possible approaches for a Python 3 migration of existing Python code running on Python 2.7. I'll evaluate which of these approaches seems suitable for a large code base and which principles should be paid attention to to increase the probability of a successful migration project. I'll use union.cms (a content management system used by German trade unions) to show the story of a successful migration project involving nearly one million lines of Python code. The story will include the obstacles we encountered and what we learned on the way. I believe that most of the discussed ideas and experience can also be used unchanged in smaller projects as the presented principles are very much the same. Goal of the presentation: You should be able to come up with a plan for migrating your Python 2 based projects to Python 3.

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name: We have nearly one million lines of Python 2 code in production – and now?: Porting a rather large code base to Python 3 – lessons learned usable for projects of any size


uk: Комп'ютерні науки

description: Sorting is one of those things that we take for granted in Python. The built-in "sorted" function knows how to sort any iterable of objects that are themselves sortable. But hiding behind that simple description is a great deal of depth. In this talk, I'll go deep into what it means to sort, and how we can sort any collection of Python data. We'll see how you can use custom functions to sort built-in data structures in new and interesting ways. And we'll see how you can design your own custom classes such that they will sort in just the way you want. After watching this talk, you'll have a better understanding of sorting, built-in data structures, function objects, and how "magic methods" affect the our Python classes. Moreover, you'll be able to write clearer, shorter, and more easily understood code. Topics I'll address in this talk: - "sorted" and Timsort - Sorting a list of simple structures - Reversing the direction with "reverse" - Custom sorting with "key" - Stable sort - Sorting a list of dicts - Using "lambda" - Using operator.itemgetter - Sorting a list of named tuples - Sorting a list of objects - Making your object sortable - The functools.total_ordering decorator This is an intermediate-level talk; I'll assume that anyone attending knows how to write functions, classes, and methods.

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name: How to sort anything: Keeping your data organized with "sorted" and custom functions


uk: Комп'ютерні науки

description: Our generation of young people in school (aged 5-18) have noticed the connection between Computer pRogramming, Technology, Success and Billionaires. On mass they are clamouring to master the skill of Computer pRogramming. It has been dubbed the ‘4th’ R’ (computer pRogramming) along with Reading, wRiting and aRithmetic. So, governments worldwide have launched initiatives to have it taught in schools from Kindergarten to all the way to high school. So, how do we best teach and motivate the next generation in acquiring this skill? We describe a successful working model for the teaching of Computer pRogamming. We have been building upon this model for a couple of years now. Last year at EuroPython 2019 in Basel, we spoke about this subject and this year we would like to share more findings with the Python community about what we are learning about the teaching of Computer Programming to children and young people. The talk will describe a case study whereby computer programming (Python) was introduced to a group of 110 young people from the ages of 11 to 18, in a U.K secondary school. The talk will include descriptions of the various teaching methodologies & the outcomes; & the challenges involved. The talk will conclude with helpful suggestions, on how to proceed in this area.

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name: Creating the Next Generations of Billionaires - Part 2: Teaching the Next Generation of Computer Trillionaires


uk: Комп'ютерні науки

description: Great, you've developed a great web API. Awesome, you are about to deploy it. But, how do you secure it and manage access? Learn about different approaches to securing a web API whether it is meant for third-party integrations or driving a modern single-page application. Not all APIs are the same, so we will explore different considerations to make when crafting a solution to handle token-based authentication, and scoping to define access levels. As one of the core developers of the async web framework Sanic, I will primarily focus on authentication and authorization tools inside Sanic to showcase how to address these issues. However, the concepts should be broadly applicable enough to take back and apply to any web API. The goal is to learn the hot spots, and identify strategies to overcome them. Core take aways will include: - how to implement various JWT strategies; - best practices for storing JWTs on a browser; and - controlling access privileges using structured scopes. Time permitting, we will even discuss some tools to be used to help ease the anxiety and make security more approachable.

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name: Overcoming access control in web APIs: How to address security concerns using Sanic


Guido van Rossum Q&A

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uk: Комп'ютерні науки

description: In the current IT scenario, the use of APIs is, without question, widely vast, popular, and significant. It seems at least prudent to ensure they are working properly, as expected. To guarantee the quality of the data that is being consumed or used. To ensure that communication between services is occurring as expected. To ensure the system does, in fact, what it should do. ScanAPI is an open-source library that was created to solve all these points. ScanAPI provides an easy way to create integration tests for REST APIs via configuration files. The three main reasons that led me to the creation of this library were: 1. To be a firefighter in a scenario where most of the reported bugs were directly related to some API contract being broken or some endpoint having a behavior different from that expected by the consumer. 2. To need to deal with APIs with outdated or mistaken documentation. 3. To need to debug an endpoint in the middle of a "chain" of other endpoints. Where I needed to recreate a state that depends on the action of other endpoints. Given a fictitious example: debug an endpoint to reserve seats on an airplane, which depends directly on the previous call of an endpoint for user registration. The ScanAPI is open-source and written in Python. However, it can be used in APIs created in any other programming language, since the tests provided by it are integration tests. With ScanAPI you can: - Use environment variables - Create variables - Chaining requests - making a request from a result of previous responses - Hide sensitive information of the requests and responses in the generated report - Write Python code - if you want, it's not necessary :) Topics I intend to address in this talk: - Motivation to create the library - What problems the library solves - How to test an API using a ScanAPI - Show the documentation generated by ScanAPI - An overview of how it is implemented - Where it is currently being used - Next steps Project Link: https://github.com/scanapi/scanapi Prerequisites: - Python - REST APIs - Integration tests

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name: ScanAPI: Automated Integration Testing and Live Documentation for your API


uk: Комп'ютерні науки

description: This project contains an open source Python library for image reconstruction in Axial Computed Tomography (TAC), based on the analytical Radon transforms of some classes of phantoms. The package is available on GitHub at the following address: https://github.com/francescat93/Exact_sinogram. The mathematical phantoms are fictitious images, composed of very simple geometric figures (ellipses, squares and rectangles) that, sampled with the Radon transform allows to build a fictitious signal, called (exact) sinogram. Using a phantom gives the advantage to test the reconstruction algorithm on a zero-noise data so the error we get is only due to numerical inaccuracies in the algorithm itself. We want to calculate two reconstructed images from the approximated and exact sinograms, obtained applying the iradon function of the Python library scikit-image on both of them. We expect a smaller error on the exact reconstructed image. This turns to be true on continuous regions, but near the discontinuities of the phantom the Gibbs phenomenon prevents us to obtain the same enhancement.

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name: The Phantom of Radon: A story of analytical sinograms


uk: Комп'ютерні науки

description: In the past, quantum computing was largely reserved for researchers, physicists, and scientists with direct access to physical quantum computing systems. But the game has changed, thanks to the cloud. Barriers to quantum computing are coming down quickly. Today, cloud access (like D-Wave’s Leap 2 quantum application environment) and improvements in quantum computing hardware, software, and developer tools are allowing programmers around the world to code on live quantum computers in real-time. Developers, students, and researchers around the world can now tap into the power of a quantum via their browser — quantum mechanical knowledge not required. Users and private companies have already built over 200 early applications on D-Wave’s computers in industries ranging from automotive to machine learning, aerospace, finance, and beyond. The quantum application era is here, and the growing quantum developer community is making it a reality. In this session, Alex Condello, Manager of Applications Development Technology and Tools at D-Wave Systems, will talk about the burgeoning quantum application development ecosystem, and how developers can start learning to code on a quantum computer today. This includes a walkthrough of Leap 2, D-Wave's new quantum cloud service equipped with hybrid solvers, and D-Wave's Ocean SDK. Alex will also explore some of the early applications that developers and companies have built to-date.

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name: Building quantum applications with D-Wave's Leap: Get started coding on a quantum computer using D-Wave's Python-based Leap cloud service.


uk: Комп'ютерні науки

description: Serverless functions-as-a-service lets you run event-triggered code without having to explicitly provision or manage infrastructure. While they are powerful, they also have limitations. In this talk, we’ll discuss how Durable Functions can overcome the limitation by extending Azure Functions to let you write stateful functions in a serverless compute environment.

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name: Durable Functions: Answer to Serverless Needs


uk: Комп'ютерні науки

description: Command-line applications and interfaces are used by both newcomers and experienced Python developers everyday. But do you know how they came to be? Hop on to this ship as we go through the CLI galaxy and look at its history, explore the CLI anatomy and discover some Python packages that can help us create them. We’ll then look at some widely used CLIs of our time. And emulate one of them by creating our own CLI using Click. Finally, we’ll package it and publish it on PyPI. Are you ready to travel faster-than-light using this ship’s Infinite Improbability Drive? Carry your towel!

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name: The Hitchhiker's Guide to CLIs in Python: Learn all about CLIs and how to create one


Morning Announcements 07/24

uk: Комп'ютерні науки

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uk: Комп'ютерні науки

description: The goal of this talk is to introduce data pipeline developers to QuantumBlack's approach for keeping data pipelines healthy and sustainable and facilitating collaboration between data scientists and data engineers by using our open source framework, Kedro. Attendees need between novice and intermediate knowledge of Python (enough to understand syntactic sugar and funargs) in order to appreciate this talk. As data continues to inform more and more business strategy, high quality, fully featured data pipelines have never been more critical. Small data scripts and single-coder science projects are not enough to keep up with the pace of day-to-day business and their ever-growing list of requirements. Now, more than ever, we need data engineers and data scientists to collaborate effectively. Yet, these two parties come with inherently competing needs. Data scientists need high data volatility and parameterization, for experimentation, and data engineers, on the other hand, need stability and performance, to deliver data. Furthermore, as pipelines grow, the cost of knowledge transfer and training new team members also increases. How can we get scientists and engineers to work well together, and sustain pipeline growth as the team also grows? For this, QuantumBlack created Kedro, a framework for writing data pipelines that addresses both the needs for flexibility and stability in its features and patterns of use. By using Kedro’s tools and operating model, we have enabled our teams to scale our single-developer, micro-pipes to industrial sized data processors with dozens of developers; all without sacrificing readability, quality, or stability. This talk will show you how.

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name: Writing and Scaling Collaborative Data Pipelines with Kedro: How to get your Data Scientists and Data Engineers to play nice, both now and in the future.


uk: Комп'ютерні науки

description: What happens when your features are done, your MVP is ready and you want to deploy your first production build? What do you do then? How do you make your first production build instead of re-using your development one? is your code ready to handle real user interactions? This talk will show a production ready checklist for your Python code; what to look for when creating a production-ready Docker image; what are the differences between development and production environments and builds. You will see how to deal with exceptions, logs, and metrics with real-world use cases.

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name: Can we deploy yet?: How to make your code production ready


uk: Комп'ютерні науки

description: Imagine you are passing through an unknown street at midnight and you find that some anti-social elements are following you. To save yourself from them you start running and look for a safe place to hide. On the way, you will find a good person and requests him to help you. He hides you in his place to protect you. When these anti-social elements visit a good person’s place and enquire about you, the good person misguides them and redirects them to some other place in order to protect you. This is exactly how deception works. In this analogy, YOU are the resources to be protected, anti-social elements are the hackers who want to gain access to the resources, and a good person is a deception technique that protects the resources from hackers by making them fall in the trap. The talk begins with an introduction to deception technology, deception types, and methods, a deceptive security life cycle. In this talk, we will demonstrate the following deception tools implemented using python language: • WebTrap (https://github.com/IllusiveNetworks-Labs/WebTrap): is designed to create deceptive webpages to deceive and redirect attackers away from real websites. The deceptive webpages are generated by cloning real websites, specifically their login pages. • DemonHunter (https://github.com/RevengeComing/DemonHunter): is a distributed low interaction honeypot with Agent/Master design Finally, we will conclude the talk with how built a deception tool and demonstrate its working. How we implemented a deception tool in python using machine learning: We designed a deception tool in python language using PyBRAIN package to model and mitigate XPath injection attacks for web services. It is known that XML can be used to store the data and this data can be queried using XPath query language. XPath is a query language, it has injection issues similar to SQL. To handle this issue, we proposed a solution, which uses a count-based validation technique and Long Short-Term Memory (LSTM) modular neural networks to identify and classify atypical behavior in user input. Once the atypical user input is identified, the attacker is redirected to fake resources to protect the critical data. Our experiment resulted in over 90% accuracy in the classification of input vectors. Outline 1. Introduction to deception, Deception types, Deception technology applicable methods and Deception Life cycle(08 Minutes) 2. Demonstration of WebTrap deception tool(04 Minutes) 3. Demonstration of DemonHunter deception tool(04 Minutes) 4. Discussion of our deception tool and demonstration(06 Minutes) 5. Conclusion and Questions(03 Minutes) Audience No experience level of Python is needed. In general, anyone can attend this talk and learn about applying deception techniques and machine learning to application security.

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name: Deceptive Security using Python: Deceiving hackers to protect your resources


uk: Комп'ютерні науки

description: Android malware analysis engine is not a new story. Every antivirus company has their own secrets to build it. With python and curiosity, we develop a malware scoring system from the perspective of Taiwan Criminal Law in an easy but solid way. We have an order theory of criminal which explains stages of committing a crime. For example, crime of murder consists of five stages, they are determined, conspiracy, preparation, start and practice. The latter the stage the more we’re sure that the crime is practiced. According to the above principle, we developed our order theory of android malware. We develop five stages to see if the malicious activity is being practiced. They are 1. Permission requested. 2. Native API call. 3. Certain combination of native API. 4. Calling sequence of native API. 5. APIs that handle the same register. We not only define malicious activities and their stages but also develop weights and thresholds for calculating the threat level of a malware. Malware evolved with new techniques to gain difficulties for reverse engineering. Obfuscation is one of the most commonly used techniques. In this talk, we present a Dalvik bytecode loader with the order theory of android malware to neglect certain cases of obfuscation. Inspired by the design principles of the CPython interpreter, our Dalvik bytecode loader consists of functionalities such as 1. Finding cross-reference and calling sequence of the native API. 2. Tracing the bytecode register. The combination of these functionalities (yes, the order theory) not only can neglect obfuscation but also match perfectly to the design of our malware scoring system. Further, we will also show a case study of Android malware and demonstrate how the obfuscation technique is useless to our engine. Last but not least, we will be open-sourcing everything (Malware Scoring System, Dalvik Bytecode Loader) during our presentation. Audience 1. Who is this talk for? - Anyone who's interested in cyber security or anyone that wants to know how to build an anti-virus engine with Python. 2. What background knowledge or experience do you expect the audience to have? - A little of Android application development and malware analysis. 3. What do you expect the audience to learn or do after watching the talk? - The Dalvik bytecode loader is written as a python module, the audiences can use this module to boost up their malware analysis. - The malware scoring system can be applied not only to Android malware but also can be applied for PE files or ELF files in other OS. The audience can copy our ideas to extend their work. - Everything’s open-sourced. Outline 1. Introduction of Malware Scoring System. First, we will detail how we decode Criminal Law to simple principles. For example, principles to define crime, penalty and the order theory of criminal, etc. And then we will detail how do we develop the order theory of android malware and other developed theories that construct the malware scoring. 2. Design Logic of the Dalvik Bytecode Loader. Once the malware scoring system were built, this will discuss the design logic of our Dalvik bytecode loader which includes our obfuscation-neglect module and bytecode register tracing module. We will also detail why the order theory of android malware succeeds at neglecting the obfuscation. 3. Quark Engine Practice - Case Study of Android Malware Next, we will practice our engine and case study through an android malware. Moreover, we will also demonstrate our obfuscation-neglect technique against obfuscation malware. 4. Future works Here, we will discuss the limitations of our engine. For example, the challenge of our Dalvik bytecode loader. Also, we will share our plans of implementing more detection techniques conquering the escape detection of malware.

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name: So, You Want to Build an Anti-Virus Engine?


uk: Комп'ютерні науки

description: Docker has become a standard tool for developers around the world to deploy applications in a reproducible and robust manner. The existence of Docker and Docker compose have reduced the time needed to set up new software and implementing complex technology stacks for our applications. Now, six years after the initial release of Docker, we can say with confidence that containers and containers orchestration have become some of the defaults in the current technology stacks. There are thousands of tutorials and getting started documents for those wanting to adopt Docker for apps deployment. However, if you are a Data Scientist, a researcher or someone working on scientific computing wanting to adopt Docker, the story is quite different. There are very few tutorials (in comparison to app/web) and documents focused on Docker best practices for DS and scientific computing. If you are working on DS, ML or scientific computing, this talk is for you. We'll cover best practices when building Docker containers for data-intensive applications, from optimising your image build, to ensuring your containers are secure and efficient deployment workflows. We will talk about the most common problems faced while using Docker with data intensive applications and how you can overcome most of them. Finally I'll give some practical and useful tips for you to improve your Docker workflows and practises. Attendees will leave the talk feeling confident about adopting Docker across a range of DS, ML and research projects. Who and Why (audience) This talk is designed for folks working in data-intensive environments (i.e. Machine Learning, Data Science, research and scientific computing) and that are either using Docker or want to learn more about how to use Docker in these environments. Attendees will leave the talk feeling confident about adopting Docker in their workflows as well as have acquired several best practices and guidelines to do this robustly. Introduction (5 minutes) About me When is Docker the right choice? Docker for all Python users: introduction to Docker in Machine Learning (ML), Data Science (DS) and research contexts The usual culprits Optimising for data-oriented application (10 minutes) Creating a data-oriented Docker image - how is this different from an app/web image? Choosing the right base image - set yourself for success Dependencies, volumes and code best practices Security and performance (10 minutes) Finding vulnerabilities in your images Image consistency and reproducibility Optimising image building - cache and image size considerations Do not reinvent the wheel - automate! (10 minutes) Consider tools to assist with Dockerfile generation - e.g. repo2docker, dokta Creating templates for projects Automating image build and publishing - e.g. GitHub actions Automated deployment strategies - going from local to deploying your containerised application Conclusions (5 minutes) Top 10 best practices when working with Docker and Python for DS/ML and research Additional resources Thanks and getting in touch

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name: Docker and Python: making them play nicely and securely for Data Science and ML


uk: Комп'ютерні науки

description: In a data science project, one of the biggest bottlenecks (in terms of time) is the constant wait for the data processing code to finish executing. Slow code, as well as connectivity issues, affect every step of a typical data science workflow — be it for network I/O operations or computation-driven workloads. In this talk, I will be sharing about common bottlenecks in data processing within a typical data science workflow, and exploring the use of parallel and asynchronous programming using concurrent.futures module in Python to speed up your data processing pipelines so that you could focus more on getting value out of your data. Through real-life analogies, you will learn about: 1. Sequential vs parallel processing, 2. Synchronous vs asynchronous execution, 3. Network I/O operations vs computation-driven workloads in a data science workflow, 4. When is parallelism and asynchronous programming a good idea, 5. How to implement parallel and asynchronous programming using concurrent.futures module to speed up your data processing pipelines This talk assumes basic understanding of data pipelines and data science workflows. While the main target audience are data scientists and engineers building data pipelines, the talk is designed such that anyone with a basic understanding of the Python language would be able to understand the illustrated concepts and use cases.

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name: Speed Up Your Data Processing: Parallel and Asynchronous Programming in Data Science


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description: Context: Today is relatively easy to create and train a conversational agent using Machine Learning Techniques, fire it up and showcase it in your computer Problem: Sharing your chatbot with the outside world is not as easy as training your models. Load Balancer, Unit Test, Integration Tests, Differential Tests ... Text Analytics and retrain the models to better serve your audience goes way beyond the simple agent that runs in the developer environment Solution: I want to show how from my experience of deploying bots to production, leveraging DevOps + DataScience skills along with an entry level knowledge of Databases, CI/CD and distributed systems you can take your prototypes to a next level, deploy, iterate and re-train your models faster. Pre-reqs: Entry level understanding of CI/CD Pipelines, NLP, jupyterhub, Version Control, Rasa

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name: Deploy your Machine Learning Bots like a boss with CI/CD: Using Gitlab Open Source tools to automate NLP models deployment


uk: Комп'ютерні науки

description: A novel method for detecting solar panels and its geometry on aerial imagery is presented. Deep Learning with PyTorch is being used for segmentation. The goal is to know the exact locations, dimensions and potential of every solar installation in Switzerland. It is shown how we labelled the data and how we found a solution to distinguish different solar panel types.

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name: Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery


uk: Комп'ютерні науки

description: Open Source has crossed frontiers and is widely used to solve problems in the health & science fields. In this talk we’ll learn about the many moving parts of continuous glucose monitoring for type 1 Diabetes as part of the open-source artificial pancreas project and building a notifier for the most used OSS continuous glucose monitoring dashboard: Nightscout.

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name: Roadmap to an Open Source Artificial Pancreas & Diabetes monitoring with Flask: Nightscout + Python + Voice + SMS - Using tech for good!