uk: Комп'ютерні науки

description: Nowadays, if you attend an event you're bound to end up with a catalogue of photographs to look at. Formal events are likely to have a professional photographer, and modern smartphones mean that it's easy to make a photographic record of just about any gathering. It can be fun to look through the pictures, to find yourself or your friends and family, but it can also be tedious. At our company get-together earlier in the year, the photographers did indeed take a lot of pictures. Afterwards the best of them were put up on our internal network - and like many people, I combed through them looking for those in which I appeared (yes, for vanity, but also with some amusement). In this talk, I'll explain how to automate finding the photographs I'm in (or at least, mostly so). I'll walk through Python code that extracts faces using OpenCV, calculates vector embeddings using imgbeddings and OpenAI, and stores them in PostgreSQL® using pgvector. Given all of that, I can then make an SQL query to find which pictures I'm in. Python is a good fit for data pipelines like this, as it has good bindings to machine learning packages, and excellent support for talking to PostgreSQL. You may be wondering why that sequence ends with PostgreSQL (and SQL) rather than something more machine learning specific. I'll talk about that as well, and in particular about how PostgreSQL allows us to cope when the amount of data gets too large to be handled locally, and how useful it is to be able to relate the similarity calculations to other columns in the database - in our case, perhaps including the image metadata.

uk: вхід без авторизації

name: How I used pgvector and PostgreSQL® to find pictures of me at a party


Xavier Thompson

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: A frequent topic about Python is performance: its interpreted nature inhibits optimisations, and the famous GIL limits parallelism (for now!). Existing Python Compilers - Cython, Numba, Codon - focus mainly on compiling small, critical bits of code to achieve linear execution speedups. As for parallelism: parallel for-loops powered by OpenMP. To parallelize highly concurrent programs with concurrent I/O and concurrent tasks, we need more. A key difference is it requires compiling everything: as soon as the Python interpreter comes into play, the GIL will make parallelism collapse. We introduce Typon, a Python-to-C++ compiler with powerful concurrency primitives powered by a crazy homemade task scheduler. It can take untyped, idiomatic Python code and output C++ code fully independent of the Python interpreter. It also provides seamless to-and-from Python interoperability, for those cases where you really just need to import numpy. In this talk we'll recount our journey so far: why we think it's important, how we're making something new out of existing bits, what we've achieved. Along the way we might delve into fun details like type inference, concurrency primitives, and C++ pretending-to-be-Python. You'll come out of this talk with some cool insights into compiler design, concurrency, and the design of Python. Knowledge of C++ not required. Knowledge of Python language inner workings helpful.

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

name: How to Build a Python-to-C++ Compiler out of Spare Parts - and Why

EuroPython

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: In today's fast-paced world, the ability to deliver new features quickly is crucial for product-oriented companies. In this talk, we'll dive into architectural patterns that optimize the delivery of multiple client implementations in complex client-server architectures. The advent of the mobile age has dramatically altered the landscape of typical client-server models. Delivering a new feature on multiple platforms is complicated and time-consuming because it requires several engineering teams to communicate extensively and separately code and test the same feature in different languages for each platform. Let's see how architectural patterns known as Backend for Frontend (BFF) and Server-driven UI can help with solving these challenges and what the limitations are. We'll explore Python optimizations, caching strategies, and SQLAlchemy preloading techniques, which were crucial to the success of the case study I will share. This talk aims to provide you with an overview of useful architectural patterns, insights on how to implement and optimize them in Python, and strategies to make your product managers happy by shortening your time to production.

uk: вхід без авторизації

name: How to deliver 3x faster with effective API design


uk: Комп'ютерні науки

description: Would you believe us if we told you that we could create a potentially dangerous virus using Python? This is theoretically possible thanks to synthetic biology, the field of biotechnology that studies how to create and modify organisms. This discipline is used, for example, to genetically modify bacteria to produce the insulin that diabetics will later use. Obviously, such a powerful tool has its possible evil side, which is what we will explore in this talk. After a little biology and genetics class, we will explain a practical example of how to use synthetic biology through a Python script to modify an existing virus and turn it into a deadly one. Thus, you as an attendee will be able to see the potential of this field and how Python can make it easier, not only in the example of the evil virus, but also in other healthcare applications.

uk: вхід без авторизації

name: How to destroy the world using Python and a synthetic virus


uk: Комп'ютерні науки

description: In the world of software development, dealing with legacy code is often a necessary evil, especially for successful, fast-growing companies. The design stamina hypothesis suggests that legacy code is a sign of success, not failure. But how do we tackle this challenge smartly? This talk delves into the often-misunderstood realm of large-scale refactoring and rewrites, presenting a nuanced approach that contrasts with the traditional 'never rewrite' dogma. We'll delve into real-world case studies where companies have successfully navigated their technical debt, uncovering crucial insights. Specifically, we will identify two key properties of these successful rewrites that can make or break your efforts. Understanding these properties enables us to strategically manage technical debt without losing our competitive edge. This session is not just a theoretical discussion but a practical guide, concluding with a decision-making quadrant to help determine the most effective approach for your team's refactor or rewrite projects. Whether you're leading a team through growth or coaching developers on best practices, this talk will equip you with a deeper understanding and actionable insights into one of the most critical aspects of software development.

uk: вхід без авторизації

name: How to sell a big refactor or rewrite to the business?


uk: Комп'ютерні науки

description: After a huge amount of work from many people, NumPy 2.0.0 has been released, the first major NumPy release since 2006! Among the many new features, several changes to both the Python API and the C API, and a great deal of documentation improvements, there was also a lot of work on improving the performance of string operations. In this talk, we'll go through the timeline of the changes and the resulting performance improvements, while also diving deep into NumPy ufunc internals and explaining topics like NumPy (parametric) dtypes, type promotion and more.

uk: вхід без авторизації

name: How we sped up NumPy’s string operations for NumPy 2.0


uk: Комп'ютерні науки

description: Want to make all your code faster? With matrices, library knowledge, and a sprinkle of creativity, you can consistently speed up multivariate Python functions by 1000x! Modal optimization requires simple axioms - arithmetic, checking a case, calling the right sklearn function, and so on. When that’s not sufficient, three core tricks - converting conditional logic to set theory, stacking vectors into a matrix, and shaping data to match library expectations - cover the vast majority of real world cases (90% of the ~400 functions we vectorized). At Bloomberg, ESG (Environmental, Social, and Governance) Scores require complex computations on large data sets. Time-series computations are fundamental for Governance - one UDF infers board support for a policy from prior cyclical votes and other time offset inputs. By rewriting the pandas backfill as a series of reductions on a 4-tensor, we reduced the runtime from 45 minutes to 10 milliseconds! Analogously, due to real world complexity, finance UDFs can end up with 100+ if/else branches in one function. With a mix of De Morgan’s laws and sparse matrix representations, we simplified the cases and achieved 1000x+ speedups. We’ll conclude with a quick overview of cutting-edge tools, and hope you’ll leave with a concrete strategy for vectorizing financial models!

uk: вхід без авторизації

name: How we used vectorization for 1000x Python speedups (no C or Spark needed!)


uk: Комп'ютерні науки

description: This is the story of a weekend project which turned into a months long challenge. After coming across a photorealistic painting made entirely out of strings, I wanted to create one on my own. But how? I decided to reverse engineer the algorithm that computes which strings to stretch and in which order. In this talk, I will show how I created a Python algorithm that produces a beautiful work of art. I will cover topics such as greedy algorithms, image processing, color spaces, performance optimization and many other challenges that I encountered while cracking the algorithm. And of course, I'll show the resulting work of art!

uk: вхід без авторизації

name: I reverse engineered a work of art, and this is what I learned


uk: Комп'ютерні науки

description: Imagine stepping into your dream job as a python data developer, ready to dive into coding and show your talent, only to run into missing database credentials that leave you idle for days due to slow interdepartmental communications and permission issues. Frustrating, right? In my talk, I'll showcase how we can make this whole process much easier. I’ll explain how using something called “Identity and Access Management” (IAM) lets everyone in a company, including machines, get to work without these annoying holdups. Surprised to hear that a machine like Airflow can have its own identity? I'll explain how we use something known as Workload Identity as a crucial part in this ecosystem integrating Airflow within our infrastructure. A central pillar of the discussion will be the role of impersonation in our setup - how it ties together various elements to enable a harmonious, secure, and maintainable infrastructure. The resulting architecture not only fosters an improved developer experience, faster project delivery, increased productivity and transparency, but also serves as a foundation for more advanced concepts such as data mesh implementation. Join me in this talk to discover the synergy of IAM, Workload Identity, and impersonation. Let's equip you with a model that promotes easy team onboarding, transparent access management, and a secure, frustration-free workspace focused on delivery. And for those interested in having their code perform consistently, whether on a local machine or in the cloud, I will share a small but powerful Docker hack to achieve things consistently no matter where your code is running.

uk: вхід без авторизації

name: Impersonation in Data Engineering: No More Credentials in Your Code!


uk: Комп'ютерні науки

description: In the domain of scientific and high-performance computing (HPC), software packages are primarily written in compiled languages such as C, C++, and Fortran, complemented by end-user APIs implemented in Python. Such packages frequently incorporate CPU-specific code (e.g. SIMD extensions) and utilise GPU-specific programming models, such as OpenMP and CUDA, to achieve enhanced performance. Despite the recent proliferation of build backends for creating pure Python packages, the distribution of Python packages containing binary extensions poses a unique set of challenges and currently lacks a standardised solution. In this talk, I will share insights and experiences gained from building portable and performant Python wheels for a set of computational neuroscience projects, aiming for compatibility and usage across a diverse of compute platforms, from desktop to large compute clusters.

uk: вхід без авторизації

name: Insights and Experiences of Packaging Python Binary Extensions


Intellectual Property Law 101

uk: Комп'ютерні науки

description: "Oh, legal is boring," most developer community thinks in this line. Yes, it is boring, but it is essential at the same time. We will demystify certain basic legal concepts the developers need to know to secure them, their code, and, most importantly, the consequences of their steps. I will go through three fundamental pillars of Intellectual property laws: Trademark, Copyright, and Patent. The talk will include real-life examples of applying all of the above. This talk targets developers and not legal experts.

uk: вхід без авторизації


Invent with PyScript

uk: Комп'ютерні науки

description: _[PyScript](https://pyscript.net/) is a platform for Python in the browser_, enabled by [web-assembly](https://webassembly.org/). It brings the rich ecosystems of [CPython](https://python.org/) and [MicroPython](https://micropython.org) to the web. _[Invent](https://invent-framework.github.io/) is a PyScript based app creation framework with complementary browser based tooling_ and is designed to be easy to learn and use, no matter if you're a beginner or expert. This talk introduces Invent, explains how it was built on top of PyScript and describes the design and architecture decisions made to ensure Python and the browser complement each other. Invent apps work anywhere a browser works and by the end of this presentation you'll be armed with all you need to know to build, deploy and extend native Python based applications running atop PyScript on all manner of platforms (mobile, tablet, laptop, desktop, [web-enabled fridge](https://www.standard.co.uk/shopping/esbest/home-garden/kitchen-appliances/best-smart-fridges-b1002542.html), [car](https://www.youtube.com/watch?v=GkhbN9B9bbs), point of sale terminal... you name it, so long as there's a browser!). This talk will be fast-paced, technical, creative, full of possibilities, may include geese

uk: вхід без авторизації


Is it me or Python memory management?

uk: Комп'ютерні науки

description: Have you ever wondered if Python memory management is playing tricks on you? Starting small, everything runs smoothly. But as your application scales, complexity grows, and memory issues rear their head. You ask yourself, "Is it me or Python memory management?"In this talk, we'll show you how Python memory works, provide tools to analyze memory usage and share practical optimization tips. Whether you're a seasoned Python developer or just starting on your Python journey, this talk is designed to provide you with techniques to overcome Python memory management challenges and write more efficient, memory-conscious code.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Retrieval-Augmented Generation (RAG) is a widely adopted technique to expand the knowledge of LLMs within a specific domain while mitigating hallucinations. However, it is not a silver bullet that is often claimed to be. A chatbot for developer documentation and one for medical advice may be based on the same architecture, but they have vastly different quality, transparency and consistency requirements. Getting RAG to work well on both can be far from trivial. In this talk we will first understand what RAG is, where it shines and why it works so well in these applications. Then we are going to see the most common failure modes and walk through a few of them to evaluate whether RAG is a suitable solution at all, how to improve the quality of the output, and when it's better to go for a different approach entirely.

uk: вхід без авторизації

name: Is RAG all you need? A look at the limits of retrieval augmented generation


uk: Комп'ютерні науки

description: The Update Framework (TUF) has been a prime reference for secure package delivery and updates for many years. Despite its popularity, integrating with existing package managers remains challenging. PEP 458 is a very good example of some of the related challenges. Authored a decade ago, it aims to protect the freshness, consistency, and integrity of packages in the Python Packaging Index (PyPI) and provide compromise resilience. Even though these goals remain largely unaddressed, PEP 458 has not made its way into production yet! With Repository Service For TUF (RSTUF), a new tool has emerged, which makes implementing and maintaining a TUF-powered package repository a black box to the repository maintainers and which has become mature enough to kick off an incremental integration in Warehouse and RubyGems. In this talk, Kairo, RSTUF's tech lead, and Lukas, TUF project maintainer, will show how RSTUF has evolved and allowed us to take a big leap towards adopting TUF in Warehouse and elsewhere. Primers on TUF, PEP 458, and Warehouse will be included.

uk: вхід без авторизації

name: It’s happening: TUF joins PyPI (Warehouse)


Keeping your projects nice and clean

uk: Комп'ютерні науки

description: Keeping your projects nice and clean helps other to understand your code better and it's crucial when you're working in teams of more than a few people. How do you achieve that? I'll talk about selected quality control tools, autoformatters, CI, but also about conventions, review process and other details of how we tackle this problem in my workplace. I'll discuss how to introduce changes gradually and keep your repository style and quality checks in sync, even when you have dozens of them. And also about what happens when you overdo it and the tools that should make your life easier actually turn into the torturing machine.

uk: вхід без авторизації


Learning to code in the age of AI

uk: Комп'ютерні науки

description: Across the industry, programmers of all levels are embracing AI and LLMs. But: it's still worthwhile to learn the foundations of coding. And there's a risk: some learners are using AIs as footguns and limiting their own growth

uk: вхід без авторизації


uk: Комп'ютерні науки

description: I started maintaining open source projects back in 2016 with tox. In 2018, I became a virtualenv maintainer. Today, this has now ballooned to 16 different packages where I'm the primary maintainer (+6 other projects where I help out). On average, these packages get more than 360 million downloads each month. In this talk, I will share my experience, and explore how I manage to pull this off while also having a primary job and a family. I will present which techniques I tried, what worked, and what did not. I will also share my views on how one should approach maintaining an open source package to avoid burnout.

uk: вхід без авторизації

name: Lessons learned from maintaining open-source Python projects


uk: Комп'ютерні науки

description: Would you like to use large language models (LLMs) in your own project, but are troubled by their tendency to frequently “hallucinate”, or produce incorrect information? Have you ever wondered if there was a way to easily measure an LLM’s hallucination rate, and compare this against other models? And would you like to learn how to help LLMs produce more accurate information? In this talk, we’ll have a look at some of the main reasons that hallucinations occur in LLMs, and then focus on how we can measure one specific type of hallucination: the tendency of models to regurgitate misinformation that they have learned from their training data. We’ll explore how we can easily measure this type of hallucination in LLMs using a dataset called TruthfulQA in conjunction with Python tooling including Hugging Face’s `datasets` and `transformers` packages, and the `langchain` package. We’ll end by looking at recent initiatives to reduce hallucinations in LLMs, using a technique called retrieval augmented generation (RAG). We’ll look at how and why RAG makes LLMs less likely to hallucinate, and how this can help make these models more reliable and usable in a range of contexts.

uk: вхід без авторизації

name: Lies, damned lies and large language models


uk: Комп'ютерні науки

description: Have you heard we've got a nice new REPL in 3.13? Let's make some music with it in real time. You've had an intense conference. How about you sit back and relax and watch a music performance constructed before your very eyes? We'll be interacting with hardware synthesizers and drum machines, and even showing visualizations, but most importantly we will be writing Python, lots of Python. Consider it a demo of PyREPL and some other features of Python 3.13.

uk: вхід без авторизації

name: Live coding music with PyREPL in Python 3.13


uk: Комп'ютерні науки

description: In my seven years as a software developer, I've primarily worked in teams composed solely of developers. However, my recent transition to a team of security researchers has opened my eyes to a crucial aspect that often goes unnoticed: log safety in applications. My exposure to the application security ecosystem and real-life security breach analysis has opened my eyes to recognize code security issues, including the prevalence of sensitive information, tokens, passwords, and payment details, in plaintext logs. This may lead to severe data breaches, financial losses, and all kinds of catastrophes. This talk will dive into the fatal mistakes developers often make that can result in the disclosure of sensitive information in logs. We'll explore the types of sensitive data in logs. I'll share my personal experiences as a developer on a security research team and shed light on the often-overlooked consequences of insecure logging practices. We'll discuss practical patterns to safeguard sensitive information in Python applications, including identifying and redacting sensitive data before it reaches log files, and implementing secure logging practices. By the end of this talk, developers will be equipped with the knowledge and tools to protect sensitive data from accidental disclosure and safeguard their applications from the perils of sensitive data exposure. Embrace the journey towards log safety and ensure your code remains secure and confidential.

uk: вхід без авторизації

name: logger.info(f"Don't Give all your {secrets} away")


Many ways to be a Python contributor

uk: Комп'ютерні науки

description: **Python** is an increasingly used language and the number of potential contributors is growing increasingly. **Newcomers** to the community have a lot of documentation to guide them to transform from mere users to contributors. There are many ways to **contribute** to Python or one of its packages or frameworks, and we will try to see some of them together. In this talk, new Python developers will discover new ways to get involved in the community, and **community members** will get ideas for getting new people involved.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Join us for an illuminating 30-minute journey into the world of design patterns at EuroPython 2024. Design patterns aren't just abstract concepts; they are the architectural blueprints that empower developers to create elegant and maintainable software solutions. In this session, we bridge the gap between theory and practice, offering practical insights for developers of all levels. We'll delve into a curated selection of design patterns, from foundational creational patterns to advanced behavioral patterns, showcasing their real-world applications and transformative impact on Python development. Through a blend of theory and practice, attendees will gain a comprehensive understanding of how to identify common design problems and apply appropriate patterns to solve them efficiently. Using engaging examples and hands-on exercises, we'll equip attendees with the knowledge and skills needed to architect cleaner, more maintainable codebases. Whether you're a seasoned veteran or a curious novice, this presentation offers a comprehensive roadmap for mastering Python design patterns and architecting software solutions with grace.

uk: вхід без авторизації

name: Mastering Design Patterns: Crafting Elegant Solutions with a Confidence


uk: Комп'ютерні науки

description: With the rise of Generative AI, developers are now able to create a wide range of applications that can generate content from simple prompts and context. In this presentation, we will explore how you can leverage the power of Visual Studio Code, GitHub, and Azure to develop, test, and deploy generative AI applications. We will discuss the latest tools and techniques for building and training generative models, and demonstrate how to build a sample application using GPT-4o, VS Code and its extensions. Additionally, we will showcase how to use GitHub for version control and collaboration, and how to deploy and manage your applications using Azure. For both beginners and veterans, join us to learn how you can master the power of generative AI to create innovative applications.

uk: вхід без авторизації

name: Mastering Generative AI: Tools and Techniques with VS Code, GitHub, Azure


uk: Комп'ютерні науки

description: Every second spent waiting for initializations and obscure delays hindering high-frequency logging, further limited by what you can track, an experiment dies. Wouldn’t loading and starting tracking in nearly zero time be nice? What if we could track more and faster, even handling arbitrarily large, complex Python objects with ease? In this talk, I will present the results of comparative benchmarks covering Weights & Biases, MLflow, FastTrackML, Neptune, Aim, Comet, and MLtraq. You will learn their strengths and weaknesses, what makes them slow and fast, and what sets MLtraq apart, making it 100x faster and capable of handling tens of thousands of experiments. This presentation will not only be enlightening for those involved in AI/ML experimentation but will also be invaluable for anyone interested in the efficient and safe serialization of Python objects.

uk: вхід без авторизації

name: MLtraq: Track your ML/AI experiments at hyperspeed


uk: Комп'ютерні науки

description: The Python C API is used to extend Python and make C libraries accessible in Python. The C API changes often forcing C extensions maintainers to update frequently their code. Also, new binary packages have to be build for each Python version. The stable ABI provides a more stable C API and only require to distribute a single binary package for all Python versions.

uk: вхід без авторизації

name: Move the Python ecosystem to the stable ABI


uk: Комп'ютерні науки

description: Multimedia processing can be very complex, especially if you want to handle most of the available codecs and formats. Fortunately, we have FFMpeg - a "complete, cross-platform solution to record, convert and stream audio and video". It is a great tool, but its CLI is quite complex and challenging to master unless you use it on a daily basis. During this talk, I will tell you what FFmpeg is and how to use it in Python without hurting yourself.

uk: вхід без авторизації

name: Multimedia processing with FFMpeg and Python


uk: Комп'ютерні науки

description: Mutation testing is a technique for systematically mutating source code in order to validate test suites. It operates by making small changes to a program’s source code and then running a test suite; if the test suite ever succeeds on mutated code then a flag is raised. The goal is to check that a system’s test suite is sufficiently powerful to detect a large class of functionality-affecting changes, thereby helping ensure that the system functions as expected. While not in widespread use, mutation testing is a fascinating topic with great potential that has valuable lessons for the broader software development community. In this talk we’ll look at Cosmic Ray, an open-source mutation testing tool for Python. Mutation testing presents some difficult and fascinating challenges - both conceptually and from an implementation point of view - so we’ll look at how Cosmic Ray addresses (or plans to address) these complexities. While some of these details will necessarily be Python-specific, there are lessons in Cosmic Ray for the development of mutation in any language. Mutation testing is still a rather exotic testing technique, but it can produce genuinely useful and surprising results. To show this, we’ll look at a number of cases where Cosmic Ray has helped developers improve their test suites and tighten up their implementations.

uk: вхід без авторизації

name: Mutation Testing in Python with Cosmic Ray


uk: Комп'ютерні науки

description: Join us for a discussion on navigating tech leadership roles in the dynamic world of technology. In this talk, we will explore the essence of managerial roles in the tech industry, examining the unique challenges faced by leaders at different levels. We'll discuss the critical decision every developer must make regarding balancing or giving up hands-on expertise with management responsibilities, as well as the challenges of transitioning from a primarily technical role to a managerial position. Additionally, we'll explore the challenges that managees may face when led by managers and tech leads who may lack management training or experience. We'll also discuss strategies for fostering a more inclusive and supportive engineering culture overall. Let's talk about management in tech from the perspective of a tech lead that identifies as a woman.

uk: вхід без авторизації

name: Navigating Tech Leadership: Challenges and Strategies


uk: Комп'ютерні науки

description: Imagine discovering that your brain is equipped in a rare, niche operation system. No wonder the standard software does not suit your hardware and you keep encountering difficult situations… Once you discover it is just software incompatibility and you update the right app versions, the world starts being more comfortable! Unfortunately, few apps only have adapted versions. It is estimated that neuroatypical people constitute even up to 15-20% of the population. According to “the geek syndrome hypothesis” - autism, but also ADHD are likely to be common in people working in the IT industry. Neurodivergent people have a chance to become wonderful specialists and bring variety to the team thanks to a slightly different perception, special interests or ability to hyperfocus. Their presence can help introduce better practices such as clear communication and transparency. On the other hand, they are at risk of having various troubles in the world adapted to the neurotypical folk. The author will present the current state of science on neurodiversity, the challenges faced by neurodivergent IT specialists and possible improvements to make the workplace more inclusive for everyone. As a neurodivergent advocate, an IT professional and a biologist with scientific mindset, she will combine her own life and career experience as a neuroatypical person with psychological knowledge which will create a unique perspective. The goal is to prove that neuroatypical people constitute a large part of the IT community and that even small actions can help meet their needs - and not only make their life easier, but also add more creativity to IT teams!

uk: вхід без авторизації

name: Neurodiversity in the IT industry. Why do YOU need to know more about it?


uk: Комп'ютерні науки

description: Domestic violence is a widespread problem, one which demands attention and policy fixes. But available data is largely unstructured, making analysis difficult for both researchers and policy makers. In this talk, I'll show you how Python helped me to retrieve, structure, and classify violence victims' testimony. I'll show which APIs and libraries allowed me to retrieve the woman's testimony from YouTube, turn their speech into text, and then analyze the text itself. You'll come away knowing not just some new Python techniques, but also how those techniques can be used to improve our society.

uk: вхід без авторизації

name: NLP Application in Cases of Violence Against Women


uk: Комп'ютерні науки

description: Ever felt like you’re navigating a data jungle, battling to survive the unexpected production problems that throw you off track? Well, you’re not alone. Staying on top of your data's health is not just smart – it's crucial. In this talk, I will share some Python tricks (methods and libraries) that you can use to defend from those wild data problems. Because let's face it, being able to effectively monitor your data, spot sneaky anomalies, and get to the bottom of them is the key to unlocking a buried treasure. First, I'll take you through the ins and outs of observability, highlighting its importance for managing both the inputs and outputs of machine learning models, as well as for overall data quality. We'll explore a range of techniques to detect anomalies, with a focus on multivariate time series data. We'll also cover how we can keep this process as computationally efficient as possible. But we won't stop at just finding these anomalies: we're on a mission to chase them down to their lair! The second part of the talk will equip you with the detective skills to perform root cause analysis and extract as much insights as possible. These discoveries can be an eye opener and the first step towards new projects and strategies. Next, we will also tackle distinguishing real anomalies from data evolution (or drift) and set up effective monitoring strategies to keep your data clean and insightful. If your interests lie in machine learning or you're simply keen on data quality, join me as we set off to unravel the mysteries of data observability. Let's learn how to keep data problems in check and when life gives you anomalies, turn them into business opportunities!

uk: вхід без авторизації

name: One analysis a day keeps anomalies away!


uk: Комп'ютерні науки

description: Declaring license metadata in Python packaging has got many pitfalls. The current standard doesn’t meet the needs of the wider public, including downstream packagers (e.g. Linux distributions). Trove classifiers are all but precise. Every build backend comes up with its own idea how to fill in the data in pyproject.toml or their custom formats. It comes hardly as a surprise that there’s an existing attempt to fix the landscape with standardization: PEP 639. In my talk I’ll outline what the proposal is about and how it’s been developing over the years. I’ll summarize the current state and the next steps. This includes the introduction of SPDX expresssion syntax, changes to the project metadata declaration, changes to the core metadata, improved glossary and some more.

uk: вхід без авторизації

name: PEP 639 - Towards licensing standardization in Python packaging


uk: Комп'ютерні науки

description: For most people that use Python, worrying about memory is not an issue. But that's not the case when you have to handle a lot of requests on a large scale. So how do you reduce memory consumption without affecting the CPU? In this presentation I'll discuss about memory management in Python from the basics, where the necessity for PEP 683 came from, and the changes introduced by it. I also intend to discuss why this PEP is so important for the language, and what we'll be able to achieve with it in the future, such as changes to the GIL and true parallelism. The talk is targeted for folks who are intermediate/advanced pythonistas. People who are just starting with Python (maybe less than 1.5 years) may feel a bit lost. Even so, curious learners are more than welcome to join, and I'll try my best to make it easy for all audiences on this advanced topic. After this presentation, participants will learn a bit more about how memory management works under the hood in python, and how it may change in the next couple of years.

uk: вхід без авторизації

name: PEP 683: Immortal Objects - A new approach for memory managing


uk: Комп'ютерні науки

description: Your data analysis pipeline works. Nice! Could it be faster? Probably. Do you need to parallelize? Not yet. Discover optimization steps that boost the performance of your data analysis pipeline on a single core, reducing time & costs. This walkthrough shows tools to identify bottlenecks via profiling, and strategies to mitigate those, demonstrating them in an example. To improve our memory and runtime performance we will use numpy, numba jit-ing and pybind11 extensions.

uk: вхід без авторизації

name: Profile, Optimize, Repeat: One Core Is All You Need™


uk: Комп'ютерні науки

description: From the team behind Pydantic, Logfire is a new type of observability platform built on the same belief as our open source library — that the most powerful tools can be easy to use. In this talk we'll introduce Logfire, then demonstrate how it can make understanding and fixing your app faster and more enjoyable with a live demo. We'll touch on some of the most useful integrations including: FastAPI, Postgres and OpenAI.

uk: вхід без авторизації

name: Pydantic Logfire — Uncomplicated Observability


uk: Комп'ютерні науки

description: In today's data-driven world, privacy stands as an essential requirements for the ethical and effective practice of data science. Moreover, the implementation of robust privacy guarantees in data analysis not only protects sensitive information, but also unlocks the potential for unprecedented democratisation of models and datasets. PySyft: github.com/OpenMined/PySyft, is a stack of open source tools that is designed to help organisations to securely collaborate with external (untrusted) individuals. By using PySyft, organisations can enable external auditors (e.g. data scientists) to use their assets, such as datasets or models, in order to conduct studies with a specific, known purpose. Data scientists can run their analysis using those assets through PySyft, and without seeing nor obtaining a copy of the assets themselves. We call this process _Remote Data Science._ PySyft is a framework for Remote Data Science. In the first part of my talk I will introduce the problem of privacy in Data Science, PETs (Privacy Enhancing Technologies), and OpenMined mission to democratise access to data and information. Afterwards, I will demonstrate how *_PySyft_* works, and how it can be used to run a machine learning experiments, with privacy guarantees.

uk: вхід без авторизації

name: PySyft: Data Science on data you are not allowed to see


Pytest Design Patterns

uk: Комп'ютерні науки

description: Proper testing of your Python application doesn't require a rewrite into hexagonal architecture (whatever it means

uk: вхід без авторизації


uk: Комп'ютерні науки

description: In the realm of Python development, achieving parallelism and harnessing the full power of modern multi-core processors is challenging. Traditionally constrained by the Global Interpreter Lock (GIL), multi-threading was not useful for true parallelism in Python, hence developers turned into multi-processing. Now this is all changing with upcoming developments. - A new per-Interpreter GIL in Python 3.12 helps to facilitate true parallelism by opening up sub interpreters to run Python codes at the same time. In addition, it is going to be easier to use these features in Python 3.13 by having support for sub interpreters in the Stdlib. - An upcoming compile option to disable-gil is opening a path to a world with NoGIL where Python threads can truly run in parallel. Which can potentially become the default in the upcoming years. Multiprocessing, sub-interpreters and multi-threading without GIL are all different ways of facilitating multi-core parallelism in Python. Each of these approaches offers a unique pathway to parallel execution, but comes with its own set of trade-offs, complexities, and suitability for different types of problems. During this talk we will explore each of these options and asses their pros and cons. Whether you're building CPU-bound high throughput applications, IO-bound services, or simply curious about the future of parallel processing in Python, this talk will help you with the knowledge to make informed decisions and leverage Python's parallel computing capabilities.

uk: вхід без авторизації

name: Python in Parallel: Sub-Interpreters vs. NoGIL vs. Multiprocessing


uk: Комп'ютерні науки

description: In the evolving landscape of serverless and cloud technologies, Python stands out as a key player for building microservices. Yet, as these systems grow, tracking their performance and catching bugs become increasingly complex. This presentation introduces OpenTelemetry, a rising standard that equips us with tools to monitor not just Python code but also vital components like databases and message queues. It's designed to blend seamlessly with Python, offering a unified method to collect, process, and share telemetry data across different parts of a distributed system. This talk starts by discussing the importance of observability in modern distributed environments. Then, we'll dive into OpenTelemetry, focusing on its Python SDK's basics. We'll walk through a hands-on example, showing how to integrate OpenTelemetry into a Python project for automated and manual tracking. Finally, we'll explore how to leverage the insights gained from OpenTelemetry for more effective system monitoring, ensuring smoother operation and easier troubleshooting.

uk: вхід без авторизації

name: Python Observability Perfected: Advanced Techniques with OpenTelemetry


uk: Комп'ютерні науки

description: When it comes to selecting a high-performance database for your application, RocksDB emerges as a top contender, offering a lightweight and efficient solution. RocksDB brings a robust set of features to the table, but what lies beneath its surface? Let's dive into the world of RocksDB with Python, uncovering the mysteries of its internal workings and exploring the principles that make data storage and retrieval seamlessly efficient. Get ready to equip yourself with the knowledge to harness the full potential of RocksDB and elevate your applications to new heights.

uk: вхід без авторизації

name: Python on the Rocks: Crafting a Smooth Blend with RocksDB


uk: Комп'ютерні науки

description: Have you ever wondered how Python gets from the first alpha version upstream to years of stability in your enterprise Linux systems? And what products and useful components are created for you along the way? In this talk, Lumír will take you through the incredible journey of Python delivery from the first alpha version shipped to Fedora Linux a couple of days after the official upstream release, through containers developers can use for testing with many old and new Python releases in their CI, to Red Hat Enterprise Linux and its main and alternative Python application streams and containers with various Python versions ready to be deployed to production environments with years of required stability. In this talk, Lumír will talk about: * Python maintainers’ focus on speed of delivery in Fedora and stability and reliability in RHEL. * How to use containers based on Fedora for early adoption of new Pythons in CI/CD pipelines. * What challenges do we face during ten years of maintenance of old Python interpreters. Come and learn how you can benefit from our efforts, use a modern development environment, and deploy your apps with guaranteed stability.

uk: вхід без авторизації

name: Python’s Journey: From Upstream to Enterprise


uk: Комп'ютерні науки

description: Python prides itself on its 'batteries included' philosophy, but beyond the well-trodden modules lie hidden gems awaiting discovery. This talk ventures into the depths of the Python standard library to unearth lesser-known features from itertools, collections, functools, typing and more. All of these tools are already built into every standard python installation and no "(uv) pip install" is necessary. Many if not all of those are likely already known to old fashioned pythonistas, but for beginners (1-2years) there will be something new to learn, while for intermediate python developers there might be at least a few more gems to discover. By delving into these underutilized modules, attendees will gain a richer understanding of Python's built-in capabilities and learn how to write more pythonic code. 00:00 Introduction 05:30 Chapter 1: Fetching data 05:42 urllib 06:48 TypedDict 09:02 Batching 09:44 islice 11:10 batched 11:50 DictWriter 15:32 Chapter 2: Cleaning data 17:22 pairwise 19:16 chain 22:20 ChainMap¹ 23:36 Multiple Context Managers 25:54 Doctest 27:44 yield from 29:44 misusing return value in generators 33:03 NamedTuple² 33:06 Chapter 3: Processing data 34:51 defaultdict 36:28 Counter 39:55 groupby 43:10 reduce / tee Corrections 22:21 ¹itertools.ChainMap is wrong: collections.ChainMap is right 33:06 ²We also need to yield one more time at the end for the case, where the last books are lost books. Maybe even yield namedtuple(book: Book, lost: bool)

uk: вхід без авторизації

name: Python Unplugged: Mining for Hidden 'Batteries


uk: Комп'ютерні науки

description: In this modern day and age, 2 things are for certain: 1. Time-to-market for our products & features matters. 2. We can easily drown in complexity and be carried away by over-engineering. Having the ability to rapidly develop prototypes and proof of concepts is very powerful, because we can iterate towards the right thing, with code. We know that we can use Django for building mature & long-lasting applications. But what about building rapid prototypes and proof of concepts? In this talk, we’ll show that Django can do that job, reliably, as well. We’ll look at what Django & the rich 3rd party ecosystem has to offer us, when it comes to building rapid prototypes. We’ll focus on topics like: 1. How to approach rapid prototyping with the correct mindset. 2. Being quick with Django models. 3. Realizing that types can be our friends. 4. Realizing that Django admin may be all the UI we need (at least, in the beginning). 5. Using HTMX where it makes sense. 6. Components in Django templates are a good idea. The talk will be practical & pragmatic, with the aim to provide good examples, derived from experience, that’ll highlight the main topics and ideas. The talk is great for both beginners, as well as seasoned Django developers. The final goal is to give clear evidence, supported by examples, that we can use Django, reliably, to rapidly build prototypes & proof of concepts. It turns out that Django is all we need.

uk: вхід без авторизації

name: Rapid Prototyping & Proof of Concepts: Django is all we need


uk: Комп'ютерні науки

description: In natural language processing, embeddings are crucial for understanding textual data. In this talk, we’ll explore sentence embeddings and their application in dialog systems. We'll focus on a use case involving the classification of dialogs. We'll demonstrate the necessity of sentence transformers for this problem, specifically utilizing one of the top-performing small-sized sentence transformers. We will show how to fine-tune this model with both labeled and unlabeled dialog data, using the SentenceTransformers Python framework. This talk is practical, packed with easy-to-follow examples, and aimed at building intuition around this topic. While some basic knowledge of Transformers would be beneficial, it is not required. Newcomers are also welcome.

uk: вхід без авторизації

name: Representation is King: The Journey to Quality Dialog Embeddings


uk: Комп'ютерні науки

description: Do you know what _RPA_ means? Or _TDD?_ Or "embedded"? At least, for sure, you know what *Python* is

uk: вхід без авторизації

name: RPA, TDD, and Embedded: A world glued together with Python!


uk: Комп'ютерні науки

description: The instruction following and in-context learning capabilities of LLMs make them suitable for tackling many NLP tasks. In this talk, we will introduce Scikit-LLM: github.com/iryna-kondr/scikit-llm, a rapidly growing, beginner-friendly library that abstracts the complexity of working with LLMs by providing a scikit-learn compatible API. We will showcase how Scikit-LLM can be utilized for solving text classification and text-to-text tasks, and will delve deeper into various methods to improve the model performance, such as prompting strategies and fine-tuning.

uk: вхід без авторизації

name: Scikit-LLM: Beginner Friendly NLP Using LLMs


uk: Комп'ютерні науки

description: Have you ever wanted to ship a script or application to a friend or client, without requiring a specific Python installation or providing complex installation instructions ? Or you want to squeeze out that last bit of juice from your Docker Python image to speed up deployment. Then eGenix PyRun is for you. PyRun is an open source, Apache-licensed, compressed, single file Python compatible run-time, which fits into merely 5 MB on disk. It can be used to ship pure Python products as a single file on Unix platforms, create Python Docker images with very small footprint to speed up deployment, or as a neat venv replacement, truly isolating applications from any OS or other Python installations, giving you a predictable target for Python applications across Unix platforms. We have been using PyRun internally at eGenix for many years and open sourced it back in 2012. This year, we are moving the project to Github and relaunching it, in order to present it to the wider open source and Python community. The talk will go into details on how PyRun is built from the Python source tree, how to create your own single file Python apps, where it can be put to good use, the roadmap we have for PyRun and what its limitations are.

uk: вхід без авторизації

name: Shipping ready-to-run Python apps without the need to install Python


Start strong!

uk: Комп'ютерні науки

description: Starting new python projects should be fun, but often we get bogged down in the details and spend several weeks fine tuning our setup; Not to mention the unproductive time spent on discussions like: * oh, you are using `ruff`? I just use `black`! * how do you set up your dev environment? * what version of `superFramework` are you running? * can you please update your PR, the tests don't pass. Let's put a stop to it! We will go through a collection of tools and techniques you can use to set up your new project for success. What to look for and how to make sure you can hit the ground running. Covered tools/techniques: Linting, testing, CI, dependency management, editor config, and some more...

uk: вхід без авторизації


uk: Комп'ютерні науки

description: The talk "State-of-the-art image generation for the masses with Diffusers" will explore the diverse applications of the open-source Python library Diffusers in the image and video generation space. The talk will showcase how Diffusers, based on diffusion models, enables fast and high-quality image and video generation, making it accessible to a wide range of users. The presentation will cover various use cases, including image inpainting, image editing, and scene composition, demonstrating the capabilities of Diffusers in enabling users to create and edit photo-realistic images with minimum effort. The audience will gain insights into the potential of Diffusers in revolutionizing the way images and videos are generated and edited, making it a must-attend session for anyone interested in the latest advancements in this field.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Maintaining code quality through effective testing becomes increasingly challenging as codebases expand and developer teams grow. In our rapidly expanding codebase, we encountered common obstacles such as increasing test suite execution time, slow test coverage reporting and delayed test startup. By leveraging innovative strategies using open-source tools, we achieved remarkable enhancements in testing efficiency and code quality. Challenges Faced: - Test Suite Execution Time: The duration of test suite execution escalated significantly as we added more tests over time, hampering development speed. - Slow Test Startup: Complex test setup led to prolonged test startup times, impeding developer productivity. - Test Coverage Reporting Overhead: Coverage tools introduced substantial overhead and impacted test performance. Solutions Implemented: - Parallel Test Execution: We applied pytest-xdist to distribute tests across multiple runners, significantly reducing test suite execution time and enabling faster development iterations. - Optimized Test Startup: Pre-installing dependencies in a Docker image and utilizing Kubernetes for auto-scaling continuous integration runners helped expedite test startup times, improving developer efficiency. For local development, we used pytest-hot-reloading to reload tests fast after code editing. - Efficient Test Coverage Reporting: Customizing the coverage tool to collect data only on updated files of pull requests minimized overhead on test coverage reporting. As a result, in the past year, our test case volume increased by 8000, test coverage was elevated to 85%, and Continuous Integration (CI) test duration was maintained under 15 minute

uk: вхід без авторизації

name: Streamlining Testing in a Large Python Codebase


Tackling Thread Safety in Python

uk: Комп'ютерні науки

description: Thread safety is often overlooked when we start with Python for developing simple scripts. But the hidden monster will be unleashed when we try to run non-thread safe code in a multithreaded setup. We will discuss the problems which can happen when seemingly good code is run in a multithreaded environment. We will walk over the concept of race coditions, how Python’s GIL currently affects multithreading and will cover steps to fix thread unsafe code using synchronization primitives.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Working on one of the major programming languages surely is a lot of fun but sometimes very weird things happen. In these moments Python stops behaving like Python and you enter a new dimension where everything is possible. And debugging what's going on in this new world of weirdness is quite a daunting task given the size of the CPython codebase. In this talk you will learn some of the most obscure, mind-bending and difficult bugs that we faced when developing CPython and how we solve them. You will also learn all the advanced tips and tricks that we used to slay these dragons so you can fight similar monsters in your own codebases or if you want to contribute to CPython itself!

uk: вхід без авторизації

name: Tales from the abyss: some of the most obscure CPython bugs


uk: Комп'ютерні науки

description: Did you know that Python can be used to test foreign code, such as Java and C? This enables writing tests for legacy code in a very few time, reducing development time and improving code coverage (e.g., using python frameworks to generate testcases for foreign code). If you have C and Java services that communicates over the network, you can use python as a glue to anticipate some integration tests directly in python.

uk: вхід без авторизації

name: Test java and C applications with python


The Art of the Pull Request

uk: Комп'ютерні науки

description: *Want to be a better teammate? Want to get your work merged faster?* For a lot of devs (especially newer ones) the important part of a PR is the code, not the structure of the PR. However, the way commits in a PR are put together to guide a reviewer can be massively impactful. This talk looks at how to effectively craft that review experience.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Python has a lot of packaging tools and what better way to solve the problem than to add another one. Rye is in many ways just another way to try to fix Python packaging, but it's also happy to not exist if something takes its place. This talk goes over all the lessons learned from writing it, what has lead to it's creation, how it's built internally and what we can do to just solve the problem once and for all.

uk: вхід без авторизації

name: The Catch in Rye: Seeding Change and Lessons Learned


uk: Комп'ютерні науки

description: I, an undercover Imposter Syndrome sufferer, masquerade as a Staff Software Engineer amid an army of coding geniuses. Imagine you're playing a video game where you're pretending to be a powerful leader, but deep down, you're convinced you're not really good enough. That's my daily life as a staff software engineer leading a team, except it's not a game, and the 'quit' button doesn't work. In this talk, I'll tell you about my hilarious misadventures and unexpected triumphs in the rollercoaster ride of being a staff software engineer and how my team still manages to create cool things despite my 'imposter' moments.

uk: вхід без авторизації

name: The Imposter Staff Engineer’s Journey to Leadership


The PyArrow revolution in Pandas

uk: Комп'ютерні науки

description: Pandas has long used NumPy for its back-end storage. But things are changing, and the future of Pandas will likely be tied closely with PyArrow. What are Arrow and PyArrow? How do they affect Pandas users today, and how will they affect us in the future? In this talk, I introduce PyArrow, tell you what it does, how we can already use it in our Pandas work, and whether that's a good idea.

uk: вхід без авторизації


The rise of the YAML engineer

uk: Комп'ютерні науки

description: In the analytics world, many of the trending data frameworks, written in Python or other languages, are adopting the declarative paradigm: users describe the desired end state, and let the system figure out the best steps to reach that state. This can be seen at many layers: data extraction, data transformation, data visualization, but also infrastructure, data quality, governance... Lots of those frameworks use YAML as the interface between the users (data engineers, data analysts and other data practitioners) and the desired system state. In this presentation, I propose to dive into the origins of the declarative paradigm for data systems, what it means for us as data practitioners, and why we're actually not becoming glorified YAML developers. I will also talk about state management and GitOps, and probably complain about YAML multiline strings.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: With the recent popularity of new programming languages that are improving the Python ecosystem, one cannot dismiss the role of "old" languages like C++, and how the new versions might stay relevant. With these ideas in mind, how do you bring a huge project, as old as Python, written in C++ to Python? If you are thinking: «...just create some bindings and call it a day» you will be surprised. On this talk, we will dive directly into the internals on how the Qt for Python project brought one of the most popular C++ frameworks into Python - but not only with 1-to1 bindings. We will discuss about build systems, packaging, limited API, interpreters support, Platform and API compatibility, Python and C++ types, ecosystem awareness, CPython code generation, communities, documentation, and many more challenging aspects of maintaining and developing a C++ library in Python. After this talk, you will not only understand why exposing an existing library from one language to another is much more than just binding them together, but also you will be able to start developing Python applications with Qt.

uk: вхід без авторизації

name: The role of C++ in the Python ecosystem: the case of the Qt framework


The truth about objects

uk: Комп'ютерні науки

description: "Everything in Python is an object." This is a profound truth about Python, but what does it mean? Is literally EVERYTHING an object? And what is an object anyway? Are objects the same as instances of a class? How do classes and types really work in Python? And what do metaclasses have to do with anything? In fact, the answers to these questions are probably not what you think they are - Python's approach to objects is different from most other languages in sometimes surprising ways. This talk will use simple live coded examples to explore how objects work in Python and clear up several common misconceptions and misunderstandings about how objects and instances, classes and types, and metaclasses all work together. Be warned - you are likely to be surprised when you learn the truth about objects in Python.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Annotating functions with typing information is commonplace nowadays. Annotations have become synonymous with typing information, even though they could be just about anything you’d want. Are there use cases for function annotations other than typing? Is that useful? Should you care? Should you stop using typing?

uk: вхід без авторизації

name: Those annotations can have things other than typing?!


uk: Комп'ютерні науки

description: In this talk, we will explore the basic concepts of Dev Containers and demonstrate how they can support your everyday development as a Python programmer, data scientist, or machine learning engineer. With Dev Containers, you can build a consistent development environment in seconds, no matter where you are or what tools you use. And you know what? The Development Container Specification is even open source. Say goodbye to the hassle of setting up your development environment from scratch every time you start a new project! We will start with a basic example and discuss how to set up a consistent Python development environment, including best practices for package management and GPU support. After this talk, you will be able to leverage the advantages of Dev Containers, allowing you to work from anywhere and be ready in seconds. If you're tired of wasting time setting up your development environment and want to unlock the power of Dev Containers, then this talk is a must-attend for you!

uk: вхід без авторизації

name: Unlock the Power of Dev Containers: Consistent Environments in Seconds!


uk: Комп'ютерні науки

description: Answer this : In critical domains like Healthcare would you prefer a Jack-of-all-trades OR one Yoda, the master? Join me on an exhilarating journey as we delve deep into the Mixture of Experts (MoE) technique which is a practical and intuitive next-step to elevate predictive powers of generalised know-it-all models. A powerful approach to solve a variety of ML tasks, MoE operates on the principle of Divide and Conquer with some less obvious limitations, pros, and cons. You’ll go through a captivating exploration of insights, intuitive reasoning, solid mathematical underpinnings, and a treasure trove of interesting examples! We'll kick off by surveying the landscape, from ensemble models to stacked estimators, gradually ascending towards the pinnacle of MoE. Along the way, we'll explore challenges, alternative routes, and the crucial art of knowing when to wield the MoE magic—AND when to hold back. Brace yourselves for a business-oriented finale, where we discuss metrics around cost, latency, and throughput for MoE models. And fear not! We'll wrap up with an array of resources equipping you to dive headfirst into pre-trained MoE models, fine-tune them, or even forge your own from scratch. May the force of Experts be with you !"

uk: вхід без авторизації

name: Unlocking Mixture of Experts : From 1 Know-it-all to group of Jedi Masters


When and how to start coding with kids

uk: Комп'ютерні науки

description: Our world is driven by technology and there are many reasons to teach our kids how to code. For example, coding allows them to develop logical reasoning skills and teaches attention to detail. Allowing children to discover how much fun coding can be supports them in their development and opens many doors for their future. But when and how should we start coding with kids? This talk will approach the question from a scientific perspective, looking into how children's brains develop, how children learn and how to best teach them coding abilities. It will answer important questions like "At what age can a child start coding?" or "What are the benefits of learning to code?". It will also present possible starting points, like learning platforms or tutorials.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: With the emergence of large generative language models there comes a problem of assigning the authorship of the AI-generated texts to its original source. This raises many concerns regarding eg. social engineering, fake news generation and cheating in many educational assignments. While there are several black-box methods for detecting if text was written by human or LLM they have significant issues. I will discuss how by watermarking you can equip your LLM with a mechanism that undetectable to human eye can give you the means of verifying if it was the true source of a generated text.

uk: вхід без авторизації

name: Which LLM said that? - watermarking generated text


uk: Комп'ютерні науки

description: As engineers, aspiring or experienced, we can become so focused on growing our technical skills that we forget about the basics. The ability to communicate well can be seen as a skill needed by leaders, managers or client-facing colleagues, but in reality it forms the basis of the quality of our work. From understanding client requirements, to code reviews and even naming variables, communication is a fundamental part of our profession and something we could all benefit from being more conscious of. In this open-to-all-levels talk we’ll discuss in what situations we should pay closer attention to our style of communication, explore the role of empathy in writing and reviewing code and cover tips and tricks for both making yourself understood and better understanding others.

uk: вхід без авторизації

name: Why communication is the best skill you can develop as a programmer


uk: Комп'ютерні науки

description: Using type hints in Python has many advantages, some of which might not be obvious at first. We will see that it allows us to explicitly encode invariants in our code, which reduces the amount of tests that we need to write, it improves development speed and maintainability, and perhaps most importantly, it can give us more confidence that our code does what we expect it to do. We will also go through code examples that will show us how to leverage typing in Python to design APIs that cannot be easily misused, to create robust programs that we can trust. Audience members are expected to be able to read and understand Python code.

uk: вхід без авторизації

name: Writing Python like it's Rust - more robust code with type hints


uk: Комп'ютерні науки

description: Everyone who writes also distributes Python code. The only reliable way to share Python code is by packaging it, any other way hurts your consumers. Packaging can be an intimidating topic most would rather avoid but following just a few best practices of packaging can make your code much easier to share, even without going through the process of uploading to pypi.org.

uk: вхід без авторизації

name: You are sharing your code wrong (and what to do about it)


Zero Trust APIs with Python

uk: Комп'ютерні науки

description: What does it take to deliver a properly secured API? When we think about API security, we first think of authentication and authorization. But there’s more to it. API security also includes protecting against SQL Injection attacks, Mass Assignment, Excessive Data Exposure, Server-Side Request Forgery (SSRS), and more. APIs are now the main attack vector on the Internet, and we gotta do something about it. Thankfully, Python boasts excellent libraries for API development, like FastAPI, the Django REST Framework, APIFlask, and more. When used properly, these libraries help us deliver secure APIs. In this talk, I’ll present a model of Zero Trust Security for APIs that applies robust data validation and sanitization across all data flows to help us deliver secure APIs. You’ll learn how your API design and implementation choices impact API security and how to discover and tackle vulnerabilities. We’ll walk through practical examples of SQL injection, mass assignment, big payload attacks, pagination attacks, and more. We’ll see how URL parameters and request payloads can become attack vectors when they’re not properly configured. You’ll also learn how to use tools like schemathesis and Spectral to automate and scale the process of detecting vulnerabilities in your APIs. By the end of this talk, you’ll be aware of the most important threats to our APIs and you’ll know how to discover and address them effectively. You’ll also get familiar with the concepts of API Security by Design, Shift-Left API Security, and Zero Trust APIs.

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

en: Information and Library Sciences

de: Geisteswissenschaften

uk: Гуманітарні науки

en: Humanities

@language: uk

uk: вхід без авторизації

name: The experience of the Kyiv-Mohyla Academy’s Scientific Library in developing data management planning skills among PhD and doctoral students

Technische Informationsbibliothek (TIB)

addressCountry: DE

addressLocality: Hanover

addressRegion: Lower Saxony

name: Ministry of Education and Science of Ukraine (MES)

name: State Scientific and Technical Library of Ukraine ( (SSTL of Ukraine)


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

uk: вхід без авторизації

name: Open research information - importance for research evaluation


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

uk: вхід без авторизації

name: Open Access journal publishing: What’s new?


de: Biologie

uk: Біологія

en: Biology

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

uk: вхід без авторизації

name: Лекція 1. Історія відкриття та дослідження стовбурових клітин (СК). Визначення і типи СК.

name: Institute for Problems of Cryobiology and Cryomedicine of the National Academy of Sciences of Ukraine

name: Projekt 'Open Education Resources with Ukraine'


Основи теорії графів.

uk: Комп'ютерні науки

uk: вхід без авторизації

name: Taras Shevchenko National University of Kyiv


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Логіка предикатів. Випереджена нормальна форма.


uk: Математика, природничі науки

uk: вхід без авторизації

name: Лекція 2 - Індуковані ПСК. Визначення плюріпотентності в системах in vitro та in vivo. Збереження ПСК в недиференційованому стані. Визнечення і функція фідеру. Теломери і їх роль в біології клітин.


uk: Математика, природничі науки

uk: вхід без авторизації

name: Лекція 3 - Регіональні стовбурові клітини. Мультіпотентні стовбурові клітини. Нейральні стовбурові клітини.


uk: Математика, природничі науки

uk: вхід без авторизації

name: Лекція 4 - Мезенхімальні стовбурові клітини.


uk: Математика, природничі науки

uk: вхід без авторизації

name: Лекція 5 - Кровотворення і стовбурові кровотворні клітини (СКК).


uk: Математика, природничі науки

uk: вхід без авторизації

name: Лекція 6 - Регуляція функцій стовбурових клітин. Тканинна інженерія. Кріоконсервування стовбурових клітин. Регенеративна медицина.


Лекція 7 - Стовбурові клітини шкіри.

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: In this talk, Dr. Oelen will present ORKG Ask, a scholarly search and exploration system. Ask uses LLMs, Semantic Search and Knowledge Graphs to provide users with a powerful and fine-grained search, going beyond the traditional keyword-based search tools. Ask is able to answer research questions and automatically extract key elements from publications. Furthermore, Ask provides the ability to upload a bibliography and apply LLMs on the contents to gain new insights. In the end, Ask is part of the next generation of scholarly search tools.

uk: вхід без авторизації

name: Presenting ORGKG Ask: a next-generation scholary search and exploration system

name: International Council for Scientific and Technical Information (ICSTI)


cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Математика, природничі науки

uk: Гуманітарні науки

description: Dr. Greiner-Petter’s talk will explore the contradictory criticism of LLMs for their poor performance in mathematical applications and their abilities to pass challenging mathematical milestones. He will further examine how LLMs sometimes succeed in high-profile math challenges, but simultaneously lacking the ability to reliably perform basic arithmetic.

uk: вхід без авторизації

name: Why ChatGPT and its friends are bad at Math


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: In this talk, Robert Underwood will share the recent progress of the AuroraGPT Team, how data contributes to the project of building a science-focused LLM with AuroraGPT, and the topics that his team sees as open questions. He will discuss the systems and data quality challenges the team tackles to prepare terabytes of scientific data and text to produce high-quality text and data for training.

uk: вхід без авторизації

name: Parsing and deduplicating Scientific Text at Scale for AuroraGPT


Automated Reinforcement Learning

uk: Комп'ютерні науки

uk: вхід без авторизації


Recent Advances in HPO

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Вступ до теорії керування. Системи ендоскопічних досліджень.

name: Kharkiv National University of Radio Electronics (KNURE)


uk: Математика, природничі науки

uk: вхід без авторизації

name: Вступ до теорії керування. Плантографія: діагностика та аналіз розподілу навантаження на стопу.


uk: Математика, природничі науки

uk: вхід без авторизації

name: Фотограмметрія для задач біомедичної інженерії


Штучний нейрон

uk: Комп'ютерні науки

uk: вхід без авторизації

name: National Aerospace University 'Kharkiv Aviation Institute'

addressCountry: UA

addressLocality: Kharkiv

addressRegion: Kharkivs’ka Oblast’


Персептрон

uk: Комп'ютерні науки

uk: вхід без авторизації

name: Projekt 'Open Education Resources with Ukraine'


Аделайн

uk: Комп'ютерні науки

uk: вхід без авторизації


Багатошарові нейронні мережі

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Бібліотеки з розроблення нейронних мереж


Бібліотека lmageAI

uk: Комп'ютерні науки

uk: вхід без авторизації


Бібліотека OpenCV

uk: Комп'ютерні науки

uk: вхід без авторизації


keywords:
Author Profiles
Linked Data
Discovery
Fachportal
Machine Learning
NLP
Wikidata

uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: The exponential growth of data on entities, persons, etc. is accompanied by a parallel surge in data quality, leading to more accurate clustering, description, accessibility, and interconnectivity. This enhancement extends beyond traditional library ecosystem data, such as authority files, to encompass a broad spectrum of non-library data sources like Wikidata, DBpedia, and OpenAlex. In this context, we present an approach that leverages linked data principles, persistent identifiers, and knowledge-based systems to construct comprehensive representations of author profiles. Rather than resorting to localized storage with isolated data silos, our approach prioritizes the direct ingestion of information from its original sources. This approach, complemented by the integration of harvested data with authors' research outputs indexed in our discovery portal EconBiz, yields a nuanced and thorough overview. The application of Natural Language Processing (NLP), text mining, and Machine Learning (ML) techniques further refines insights, offering diverse perspectives and deepening the understanding of authors. Author profiles provide a multifaceted gateway to understanding a scholar's research contributions. Visual representations enable quick identification of expertise areas and output. Thesaurus- or ML-enriched word tags enhance search capabilities, allowing users to refine queries based on semantically related terms. Co-authorship network visualizations and other features, such as “related authors” expand exploration options, uncovering scholars with similar research interests who are not necessarily co-authors. Additionally, the discovery of other persistent identifiers unlocks new avenues for profile enrichment with diverse information, including citation metrics and research data. We will present the main functionalities of the EconBiz Author Profiles including a glimpse of new developments.

uk: вхід без авторизації

name: Unveiling Scholarly Insights: The Role of Enhanced Author Profiles in Leveraging Linked Data and Persistent Identifiers

name: Verein Deutscher Bibliothekarinnen und Bibliothekare e. V. (VDB)

name: Berufsverband Information Bibliothek e. V. (BIB)


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

@language: de

uk: вхід без авторизації

name: Praxis und Recht vereint: Die (neue) gute wissenschaftliche Praxis für das Forschungsdatenmanagement


name: NFDI for Data Science and Artificial Intelligence

keywords:
Künstliche Intelligenz
Large Language Models
Research Methods

uk: Комп'ютерні науки

description: Dr. Jennifer D'Souza's keynote at the "Artificial Intelligence and Knowledge Graphs for Scholarly Data Summer School Day 2024 (AIKG-SD 2024)" hosted by the AIX lab at Leuphana University Lüneburg and NFDI4DataScience. The talk entitled "Harnessing Large Language Models as Research Assistants and in Science" provides a glimpse into her recent work applying LLMs for scientific information extraction, empirically testing the readiness of LLMs for ontology construction, and their potential in evaluating complex scientific tasks.

uk: вхід без авторизації

name: Harnessing Large Language Models as Research Assistance in Science - Jennifer D'Souza @AIKG-SD 2024

Leuphana Universität Lüneburg

addressLocality: Lüneburg


keywords:
Künstliche Intelligenz
Large Language Models
Knowledge Graphs
Knowledge Graph Embeddings

uk: Комп'ютерні науки

description: Dr. Genet Asefa Gesese's tutorial at the "Artificial Intelligence and Knowledge Graphs for Scholarly Data Summer School Day 2024 (AIKG-SD 2024)" hosted by the AIX lab at Leuphana University Lüneburg and NFDI4DataScience. Her talk entitled "Introduction to Knowledge Graphs, Knowledge Graph Embeddings, and Large Language Models" provides beginner-level explanations of these concepts with hands-on programming examples.

uk: вхід без авторизації

name: Intro to Knowledge Graphs, KG Embeddings & Large Language Models - Genet Asefa Gesese @AIKG-SD 2024


uk: Комп'ютерні науки

description: Dr. Allard Oelen's and Dr. Yaser Jaradeh's presentation at the "Artificial Intelligence and Knowledge Graphs for Scholarly Data Summer School Day 2024 (AIKG-SD 2024)" hosted by the AIX lab at Leuphana University Lüneburg and NFDI4DataScience. Their talk entitled "Open Research Knowledge Graph - Assistant for Scientific Knowledge" provides a detailed introduction to the LLM-based assistant ORKG ask that allows to query content-related information from a vast corpus of research articles via a natural language interface. Different functionalities and technical details of the frontend and backend (LLMs, Knowledge Graphs, Retrieval Augmented Generation/RAG, Semantic Searc) are explained.

uk: вхід без авторизації

name: Open Research Knowledge Graph - Allard Oelen & Yaser Jaradeh @AIKG-SD 2024


keywords:
Wikibase
Research Data Management
FAIR
Open Science

uk: Комп'ютерні науки

description: Kolja Bailly's tutorial at the "Artificial Intelligence and Knowledge Graphs for Scholarly Data Summer School Day 2024 (AIKG-SD 2024)" hosted by the AIX lab at Leuphana University Lüneburg and NFDI4DataScience. His talk entitled "Wikibase4Research" presents ways in which this tool by the TIB Open Science Lab supports researchers in dealing with Mediawiki software for knowledge bases such as Wikibase and facilitates better and FAIR Research Data Management. The talk includes a live demonstration with beginner-friendly usage instructions.

uk: вхід без авторизації

name: Wikibase4Research - Kolja Bailly @AIKG-SD 2024


keywords:
NFDI
Research Data Management
FAIR
Open Science

uk: Комп'ютерні науки

description: Christine Hennig's talk at the "Artificial Intelligence and Knowledge Graphs for Scholarly Data Summer School Day 2024 (AIKG-SD 2024)" hosted by the AIX lab at Leuphana University Lüneburg and NFDI4DataScience. This talk gives an overview of the different services, offers and active work areas of the DFG-funded NFDI4DataScience project.

uk: вхід без авторизації

name: Introducing NFDI4DataScience - Christine Hennig @AIKG-SD 2024


uk: Комп'ютерні науки

description: Dr. Jens Dierkes' presentation at the "Artificial Intelligence and Knowledge Graphs for Scholarly Data Summer School Day 2024 (AIKG-SD 2024)" hosted by the AIX lab at Leuphana University Lüneburg and NFDI4DataScience. His talk entitled "How is the NFDI actually Reaching People Beyond NFDI?" discusses the challenges research communities are facing in adapting to the quickly changing landscape of research data management practices and tools and suggests paths forward.

uk: вхід без авторизації

name: How is the NFDI actually Reaching People Beyond NFDI? - Jens Dierkes @AIKG-SD 2024


uk: Комп'ютерні науки

description: The presentation will introduce the Ethical Data Initiative. The Ethical Data Initiative provides a neutral space to bring together diverse actors and stakeholders, shaping the future of data governance. In doing so, we aim to increase equality and inclusivity in the data space; building data confidence and empowering the digital citizens of tomorrow. The presentation will also share information about the Campaign for Data Ethics in Education. The Campaign advocates for the integration of data ethics in all higher education courses focused on data science and research. It aims to educate the next generation of data and research professionals about their legal and ethical obligations when it comes to using, reusing, and sharing data.

uk: вхід без авторизації

name: The Ethics of AI and Data in Higher Education


keywords: AI Safety

uk: Комп'ютерні науки

description: AI systems that learn from data present a unique challenge for safety, as there is no specific design artifact, model, or code to analyse and verify. The safety assurance challenges become even more complex in cooperative intelligent systems, like collaborative robots and autonomous vehicles. These systems are often loosely interconnected, allowing them to form and dissolve configurations dynamically. Evaluating the consequences of failures in largely unpredictable configurations is a daunting task. Intentional or unintentional interactions between systems, along with newly learned behaviours and varying environmental conditions, can lead to unpredictable or emergent behaviours. Achieving complete safety assurance of such systems of systems at the design stage through traditional model-based methods is unfeasible. In this talk, I will explore these challenges and introduce executable causal models and statistical techniques that may help address these emerging issues.

uk: вхід без авторизації

name: Safety of AI Systems with Executable Causal Models and Statistical Data Science


Othering

de: Humanmedizin/Gesundheitswissenschaften

uk: Медицина / Науки про здоров'я

en: Human Medicine / Health Sciences

de: Erziehungswissenschaften

uk: Педагогічні науки

en: Educational Sciences

de: Rechts-, Wirtschafts- und Sozialwissenschaften

uk: Право, економіка та суспільні науки

en: Law, Economics and Social Sciences

uk: вхід без авторизації


Automatic ML using Circuits

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: The “CapData Opéra” project, initiated by ROF (Réunion des Opéras de France – French Opera Association) and supported by the French Ministry of Culture, uses Semantic Web technologies to share cultural data with the public and the artistic community. The aim is to aggregate data produced by various domain actors to make it globally searchable. This highlights previously invisible data, such as the exchange of creative works and performances between opera houses. To achieve this, an ontology has been designed to define a common vocabulary and implement data interoperability objectives. This ontology is aligned with schema.org, and we are working to align additional models. A set of SHACL rules has been created to validate the data before publication. A dedicated tool, Rodolf, has been developed to monitor the RDF publishing process. This tool is used to execute the process and track which sources have been uploaded to the SPARQL endpoint, including upload times and any errors encountered. Exporting RDF data can be challenging for institutions unfamiliar with Semantic Web technologies, so a dedicated Software Development Kit (SDK) has been developed to assist web developers in exporting CapData RDF data even if they lack experience in this area. In this presentation, we aim to share with the SWIB community the objectives and solutions we have found to federate heterogeneous data. We will present feedback on this project, focusing on technical and management aspects, and then describe the results we have achieved and the future of this project.

uk: вхід без авторизації

name: CapData Opéra: ease data interoperability for opera houses

name: ZBW - Leibniz-Informationszentrum Wirtschaft

addressLocality: Kiel

addressRegion: Schleswig-Holstein


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: E-LAUTE (https://e-laute.info/) is creating a comprehensive edition of German renaissance lute tablatures (GLT), a historically widespread music notation that has been largely neglected by modern research. It interlinks music and textual encodings, notation images, audio, semantic annotations, and bibliographic metadata by using open data formats and Linked Data throughout the entire process. Additionally, it builds on research data and information architectures provided by the Technical University of Vienna (research workflow management) and the Austrian National Library (ÖNB; GAMS digital edition platform and triplestore). We are extending the ÖNB platform with facilities for incorporating multifaceted music information, and we are augmenting the Music Encoding Initiative’s (MEI) existing XML schema for the representation of GLT documents. We contextually enrich the MEI encodings through interconnection with textual encodings of contemporary lyrics and instructional material, IIIF facsimile images, audio recordings (produced both project-internally and externally), and additional metadata. To do so, we apply Linked Data ontologies, XML transclusions between encoding schemas, and Web Annotations for external contributions through decentralized Solid pods. We aim to create a central hub for managing the enriched data and for publishing the results in uniform and state-of-the-art formats (e.g. JSON-LD), providing open APIs (e.g. SPARQL) and contributing innovative approaches to music informatics and musicological research, thus serving the needs of music researchers, practitioners and enthusiasts alike.

uk: вхід без авторизації

name: E-LAUTE: establishing a music edition platform using Semantic Web technologies


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: The last few years, large language models have profoundly impacted many research topics and product teams. From applications in health care to the creation of new soda flavors, Artificial Intelligence has captured the imagination of many people. Even though some of the initial enthusiasm and promises of large language models may have been somewhat exaggerated, it is clear that generative AI is a technology that will bring a massive impact that is still difficult to predict. In areas such as libraries, bibliography, healthcare, finance, science metrics, and many others, we have invested heavily in structured knowledge representations, such as metadata and knowledge graphs, and it is not immediately clear how Semantic Web technologies and other structured knowledge representations will fit into a world that is being rapidly transformed by the deployment of large language models. In this talk we will work on some of the answers how these two technologies might evolve and co-evolve. We will explore the weaknesses and strengths of the different approaches, and aim to identify the opportunities where they may complement each other. We may dream what may lay beyond knowledge graphs and metadata, and how the advances in language models might allow us to reach bold new frontiers in knowledge representation which might not have been accessible before.

uk: вхід без авторизації

name: Keynote: How knowledge representation is changing in a world of large language models


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Initiated in 2021, the University of Washington Libraries have committed to using LRM/RDA/RDF (RDA as Linked Data) to represent RDA description sets. While LRM/RDA/RDF has not been widely adopted and few test datasets exist, this could change rapidly if libraries transform legacy MARC data to LRM/RDA/RDF. As they near completion of Phase 1 of the project, presenters will describe a groundbreaking mapping and conversion project between the MARC21 bibliographic standard and LRM/RDA/RDF. In addition to describing the cross-organizational team, the presentation summarizes the scope and strategy of the first phase of the project, describes key discussions, highlights important decisions, and shares project workflows. Challenges with aggregate works and expressions, ambiguously-described entities, reproduction manifestations, inconsistent cataloging practices, and more are discussed. Advance releases of project deliverables are shared ahead of their anticipated official publication at the end of 2024, including mappings in .csv format, XSLT transformation code, and sample transformation data in the original MARC21 format and the resulting LRM/RDA/RDF which will be published in a Wikibase Cloud instance. Project members hope that Phase 2 of the project will address entity reconciliation and identity management as well as more complex aggregate types. More information about the open project can be found in the following GitHub repository: https://github.com/uwlib-cams/MARC2RDA

uk: вхід без авторизації

name: MARC21 bibliographic to LRM/RDA/RDF mapping and conversion project


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Given that cataloguing policies have an impact on the interoperability of bibliographic data, the National Library of Greece (NLG) initiated its efforts to increase the interoperability of Greek bibliographic data by selecting a new set of standards to implement, and by changing its cataloguing policies and workflows. The set of selected standards consists of the IFLA/LRM model, the official RDA rules, the MARC21 format (Linky Marc approach) for the Koha integrated library system, and the RDA/RDF format for the Wikibase prototype. The presentation summarizes the key decisions and practices applied in everyday business to integrate Linked Data in MARC21 records and to enable future transformations of those records to Linked Data using the RDA/RDF vocabulary. As a proof of concept, the NLG develops and maintains a Wikibase prototype where NLG data is transformed, mapped, and linked to other Linked Data sources. This is an ongoing process that, in each iteration, focuses on a specific RDA entity. So far, the authority records describing RDA Persons have been represented using RDA/RDF. The next RDA entity to be transformed and mapped using the LRM/RDA conceptualizations and vocabularies is the RDA Nomen entity. Nomens (e.g., pseudonyms) related to the RDA Persons will be incorporated, aiming to provide integrated displays of a Person and the names (Nomens) that this person has used. The presentation will conclude with a short demonstration of the Wikibase prototype and a discussion of the next steps for transforming data and further developing the prototype.

uk: вхід без авторизації

name: It is not all Greek anymore: use of LOD to increase the interoperability of data created by the National Library of Greece


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: ANTELOPE (Annotation, Terminology Lookup and Personalization) is a comprehensive annotation service developed at TIB. The service serves as a single point of access to a plethora of terminology sources across the Semantic Web. At that, it bundles several machine learning approaches to alleviate retrieval of candidate entities for annotation. The service is available through an integrated user interface, as well as a customisable API. Comprehensive user research within the NFDI4Culture community (German national research data infrastructure for the cultural domain) has revealed a demand for services aiding the annotation process in GLAM: Retrieval of entities from different terminology searches based on a simple term, fulltext, or image is a specialised and time-consuming task. ANTELOPE supports these tasks with a lean interface that abstracts state-of-the-art natural language processing, classification, and information retrieval techniques. More precisely, ANTELOPE currently provides three search interfaces: Straightforward, terminology search applies a given term to designated terminology sources and presents results in a distinctive hierarchy graph. Entity recognition accepts fulltext input that supplies contextual information for narrowed down results. Image recognition accepts image files instead and applies powerful image classifiers to bridge multimedia approaches. Ultimately, there will be a universal results interface that allows entity selection, storage and export. We are currently working on integrating ANTELOPE functionality in research frameworks, such as TS4NFDI and Wikibase to make usage as seamless as possible.

uk: вхід без авторизації

name: ANTELOPE: open source service for accessible semantic annotation in GLAM


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: The increasing volume of grey literature, such as reports produced by public sector organizations and academia, poses significant cataloguing, discoverability, and accessibility challenges in digital libraries. To help address these challenges, the National Library of Norway (NLN) and the National Library of Finland (NLF) have explored different strategies to automatically extract bibliographic metadata from PDF files. This presentation will first discuss METEOR, an open-source tool developed by the NLN that uses rule-based logic and keywords and is already integrated in the production workflow as a suggestion engine for librarians. Meanwhile, the NLF is exploring the potential of fine-tuned, locally hosted large language models for extracting bibliographic metadata. The strengths and weaknesses of both approaches are analyzed, as well as the common obstacles they face. This talk will also present our joint efforts to prepare high quality datasets for training and evaluation of metadata extraction systems along with newly developed metrics suited to the task. Finally, the discussion will focus on the integration of external catalogues and authority registries in these processes, enabling the use of persistent identifiers for entities in the metadata. Our presentation seeks to share practical solutions, promote methodology exchange, and inspire community collaboration.

uk: вхід без авторизації

name: Automating metadata extraction and cataloguing: experiences from the National Libraries of Norway and Finland