Lmst

the source

#lighting #lightingequipment
#artificiallight #lightingdesign

#ise2025 #integratedsystemseurope

#blackandwhite #blackandwhitephotography #bnw #bnwphotography #monochrome #highcontrast #bnw_of_our_world @bnw_of_our_world

#barcelona_photographers

#fotokomunidad #fotok @fotokweb

#sonya7ii #sonyalpha #sonyphotography

#sigmalens #sigmaart #sigma2470art #sigma2470

#photography #darktableedit

The #ISE2025 lecture is over. Today our students will write the final exam. FIngers crossed. Meanwhile - as our favorite coffee place is on vacation - Cappuccino and Zupfkuchen (highly recommended) together with the @fizise TA team at Intro Café :)

#coffeechallenge #lecture #academiclife @sourisnumerique @enorouzi @GenAsefa @fiz_karlsruhe @KIT_Karlsruhe

A dark brown cup of Cappuccino. The Cappuccino shows some Latté-Art in the form of a heart. Next, a piece of Zupfkuchen on a plate with a fork beside. In the upper left corner of the picture, we see the fragment of a Tunesian style ceramic ash tray (very colorful). On the table surface (mace of wood) there is the number 20 (as a helper for the service, when you are ordering your drink/cake inside)

In our last #ISE2025 lecture last week, we were discussing what makes a node "important" in a knowledge graph. A simple heuristics can be borrowed from graph theory or communication theory: Degree Centrality

Interestingly, in Wikidata In-degree centrality states Jane Austen to be to most "important" female author, while Out-degree centrality claims J.K. Rowling as being more "important" ;-)

#knowledgegraphs #semanticweb #graphtheory #feminism #eyeofthebeholder @sourisnumerique @enorouzi

Slide from the ISE2025 lecture, showing indegree centrality measures for female authors derived from Wikipedia pages that are featuring an article about this author. For In-degree centrality, Jane Austen qualifies as the "most important" female author :) .

One of our final topics in the #ISE2025 lecture were Knowledge Graph Embeddings. How to vectorise KG structures while preserving their inherent semantics?

#AI #KGE #knowledgegraphs #embeddings #semanticweb #lecture @fizise @tabea @sourisnumerique @enorouzi @fiz_karlsruhe

4. Basic Machine Learning / 4.9 Knowledge Graph Embeddings
The slide visualises the process of knowledge graph embeddings creation. starting out with a kg, scoring function, loss function, and negatives generation are represented, which are responsible for preserving semantics in the process of vectorisation. The created vectors then serve as representations for entities and properties in downstream tasks, as e.g. classification or question answering.

This week's ISE 2025 lecture was focussed on artificial neural networks. In particular, we were discussing how to get rid of manual feature engineering and doing representation learning from raw data with convolutional neural networks.

#AI #ArtificialNeuralNetworks #cnn #deeplearning #machinelearning #ise2025 @fizise @fiz_karlsruhe @sarahjamielewis @enorouzi

Slide from the ISE 2025 lecture on Neural Networks and Deep Learning.
Imagine you want to detect cats in images. Of course you have to consider that not every cat looks alike. Moreover, its position in the picture, the distance (size), the illumination/lighting, potential occlusion, etc. have to be considered. For this reason, originally well suited features had to be extracted from the images, which allowed to consider all these requirements. Nowadays, there is no need for this manual feature engineering anymore. Convolutional neural networks "learn" those features by applying convolution (and pooling) filters.

DNA

#lighting #lightingequipment
#artificiallight #darkbackground #lamplovers #lamp #lampdesign #lightingdesign

#ise2025 #integratedsystemseurope

#barcelona_photographers

#barcelona

#sonya7ii #sonyalpha #sonyphotography

#sigmalens #sigmaart #sigma2470art #sigma2470

#photography #darktableedit

microworlds

#lighting #lightingequipment
#artificiallight #darkbackground #lamplovers #lamp #lampdesign #lightingdesign #microworlds

#ise2025 #integratedsystemseurope

#barcelona_photographers

#sonya7ii #sonyalpha #sonyphotography

#sigmalens #sigmaart #sigma2470art #sigma2470

#photography #darktableedit

In the ISE2025 lecture today, our students learned about unsupervised learning on the example of k-Means clustering. One nice hands-on example is image colour reduction based on k-means clustering, as demonstrated in a colab notebook (based on the Python DataScience Handbook by Vanderplus)

colab notebook: https://colab.research.google.com/drive/1lhdq2pynuwJKoXbspydECuWcPRw3-xxn?usp=sharing
Python DataScience Handbook: https://archive.org/details/python-data-science-handbook.pdf/mode/2up

#ise2025 #lecture @fizise @sourisnumerique @enorouzi #datascience #machinelearning #AI @KIT_Karlsruhe

Slide from the Information Service Engineering Lecture 2025, Basic Machine Learning 02, k-Means Clustering. Heading: k-Means Clustering Hands-On. Depicted is the page from the colab notebook with the example image of a flower (in 24bit colour space) and the subsequently created 16 colours image via k-Means Clustering. As the 16 colours were chosen from the entire 24-bit colour space (palette), the differences are not significant for the human eye.

deal

#lighting #lightingequipment
#artificiallight #lamplovers #lamp #lampdesign #lightingdesign

#ise2025 #integratedsystemseurope

#hands #fog #backlit #streetphotography

#blackandwhite #blackandwhitephotography #bnw #bnwphotography #monochrome #highcontrast #bnw_of_our_world

#barcelona_photographers

#sonyalpha #sonyphotography

#sigmalens #sigmaart #sigma2470art

#photography #darktableedit

Tomorrow, we will dive deeper into ontologies with OWL, the Web Ontology Language. However, I'm doing OWL-lectures now for almost 20 years - and OWL as well as the lecture haven't changed much. So, I'm afraid I'm going to surprise/dissapoint the students tomorrow, when I will switch off the presentation and start improvising a random OWL ontology with them on the blackboard ;-)

#ise2025 #OWL #semanticweb #semweb #RDF #knowledgegraphs @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi

Slide from the OWL slide deck of Information Service ENgineering 2025. An example for a nominal in OWL is depicted, i.e. a class that is defined by enumerating its members. The example is "MaddAddamTrilogyBook", which refers to a trilogy of novels by Margaret Atwood, It is defined to consist out of "Oryx and Crate, The Year of the Flood, and Madd Addam. The book covers of the three books are shown for an illustration.

@lysander07 What I like about this view is that info is not necessarily complete. Typical real-world data. So, some queries will not show everything there is to know about a topic while a lot of info may be for other things. Though it can be extended - "pay as you go".

Some students may also be interested in expressing their research output as a #KnowledgeGraph on its own right - playing an important role in #ScholarlyCommunication

eg view: https://dokie.li/?graph=https://csarven.ca/linked-research-decentralised-web

seeAlso alt text.

#ISE2025

Graph view of http://csarven.ca/linked-research-decentralised-web based on underlying structured data (in RDFa) with legend expressing number of statements (3148) and uniqur nodes (2022), as well as information about the tool generated it ( https://dokie.li/ ) and the license (CC BY 4.0). The graph depicts connected nodes in different colours, with their general class, internal/external references, citations, social connections, datasets, requirements, advisements, specifications, policies, events, slides, and concepts.

In today's ISE 2025 lecture,, we will introduce SPARQL as a query language for knowledge graphs. Again, I'm trying out 'Dystopian Novels' as example knowledge graph playground. Let's see, if the students might know any of them. Wtat do you think? ;-)

#dystopia #literature #ise2025 #semanticweb #semweb #knowledgegraphs #sparql #lecture @tabea @sourisnumerique @enorouzi

Example knowledge graph for the ISE 2025 lecture. The novels represented in this graph are:
- George Orwell: Nineteeneightyfour
- Harry Harrison: Make Room! Make Room!
- Octavia E. Butler: Parable of the Sower

Back in the lecture hall again after two exciting weeks of #ESWC2025 and #ISWS2025. This morning, we introduced our students to RDF, RDFS, RDF Inferencing, and RDF Reification.

#ise2025 #semanticweb #semweb #knowledgegraphs #rdf #reasoning #reification #lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe @sourisnumerique @tabea @enorouzi

Slide from the ISE 2025 lecture on Resource Descriptiuon Framework (RDF) as simple data model. The slide is showing a small knowledge graph, indicating that Climate Change was explained by Eunice Newton Foot in 1856 as well as by John Tyndall in 1859. To represent this n-ary (multi-valued) relations, we are using so-called blank nodes, representing an "explanation" each, which bundles the discoverer and the discovery date. This is done via "dereferencable" blank nodes here.

Last week, we continued our #ISE2025 lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models.
Benefits of NLMs:
- Capturing Long-Range Dependencies
- Computational and Statistical Tractability
- Improved Generalisation
- Higher Accuracy

@fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi #llms #nlp #AI #lecture

The image illustrates the architecture of a Neural Language Model, specifically focusing on Word Vectors II - Neural Language Models. It is part of a presentation on Natural Language Processing, created by the Karlsruhe Institute of Technology (KIT) and FIZ Karlsruhe, as indicated by their logos in the top right corner.

The diagram shows a neural network processing an input word embedding, represented by the phrase "to be or not to." The input is transformed into a d-sized vector representation of the context "to be or not to" by a neural network. This vector is then passed through a linear layer, which maps the vector to a size equal to the vocabulary size (|V|), represented by a series of circles labeled "tokens." The output of the linear layer is fed into a softmax function, which generates a probability distribution over the next token, denoted as P(* | "to be or not to"). The diagram also includes annotations explaining the process, such as "get probability distribution of next token" and "context processing (previous tokens)." The overall layout is clear and educational, with arrows indicating the flow of information through the model.

Provided by @altbot, generated privately and locally using Ovis2-8B

sauron

#lordoftherings #sauron #sauroneye

#ise2025 #integratedsystemseurope

#light #lighting #artificiallight

#lamplovers #lamp #lampdesign
#lightingsolutions #lightingdesign

#sonya7ii #sonyalpha

#sigmalens #sigmaart #sigma2470art #sigma2470

#photography #darktableedit

In the #ISE2025 lecture today we were introducing our students to the concept of distributional semantics as the foundation of modern large language models. Historically, Wittgenstein was one of the important figures in the Philosophy of Language stating thet "The meaning of a word is its use in the language."

https://static1.squarespace.com/static/54889e73e4b0a2c1f9891289/t/564b61a4e4b04eca59c4d232/1447780772744/Ludwig.Wittgenstein.-.Philosophical.Investigations.pdf

#philosophy #wittgenstein #nlp #AI #llm #languagemodel #language #lecture @fiz_karlsruhe @fizise @tabea @enorouzi @sourisnumerique #AIart

An AI-generated image of Ludwig Wittgenstein as a comic strip character. A speech bubble show his famous quote "The meaning of a word is its use in the language."
Bibliographical Reference: Wittgenstein, Ludwig. Philosophical Investigations, Blackwell Publishing (1953).
Ludwig Wittgenstein (1889–1951)

Generating Shakespeare-like text with an n-gram language model is straight forward and quite simple. But, don't expect to much of it. It will not be able to recreate a lost Shakespear play for you ;-) It's merely a parrot, making up well sounding sentences out of fragments of original Shakespeare texts...

#ise2025 #lecture #nlp #llm #languagemodel @fiz_karlsruhe @fizise @tabea @enorouzi @sourisnumerique #shakespeare #generativeAI #statistics

Slide from the Information Service Engineering lecture 04, Natural Language Procerssing 03, 2.9 Language Models, N-Gram Shakespeare Generation.
The background of the slide shows an AI-generated portrait of William Shakespeare as an ink drawing. There are 4 speech-bubbles around Shakespeare's head, representing artificially generated text based on 1-grams, 2-grams, 3-grams and 4-grams: '
1-gram: To him swallowed confess hear both. Which. Of save on trail for are ay device and rote life have Hill he late speaks; or! a more to leg less first you enter.
2-gram: Why dost stand forth thy canopy, forsooth; he is this palpable hit the King Henry. Live king. Follow. What means, sir. I confess she? then all sorts, he is trim, captain
3-gram: Fly, and will rid me these news of price. Therefore the sadness of parting, as they say,’tis done. This shall forbid it should be branded, if renown made it empty.
4-gram: King Henry. What! I will go seek the traitor Gloucester. Exeunt some of the watch. A great banquet serv’d in; It cannot be but so.

The magic happens somehow at 4-grams, basically because it IS Shakesperare ;-)

In our #ISE2025 lecture last Wednesday, we learned how in n-gram language models via Markov assumption and maximum likelihood estimation we can predict the probability of the occurrence of a word given a specific context (i.e. n words previous in the sequence of words).

#NLP #languagemodels #lecture @fizise @tabea @enorouzi @sourisnumerique @fiz_karlsruhe @KIT_Karlsruhe

Slide from the Information Service Engineering 2025 lecture, 03 Natural Language Processing 02, 2.9, Language MOdels:
Title: N-Gram Language Model
The probability of a sequence of words can be computed via contitional probability and the Bayes Rule (including the chain rule for n words). Approximation is performed via Markov assumption (dependency only on the n last words), and the Maximum Likelihood estimation (approximating the probabilities of a sequence of words by counting and normalising occurrences in large text corpora).

This week, we were discussing the central question Can we "predict" a word? as the basis for statistical language models in our #ISE2025 lecture. Of course, I wasx trying Shakespeare quotes to motivate the (international) students to complement the quotes with "predicted" missing words ;-)

"All the world's a stage, and all the men and women merely...."

#nlp #llms #languagemodel #Shakespeare #AIart lecture @fiz_karlsruhe @fizise @tabea @enorouzi @sourisnumerique #brushUpYourShakespeare

Slide from the Information Service Engineering 2025 lecture, Natural Language Processing 03, 2.10 Language Models. The Slide shows a graphical portrait of William Shakespeare (created by midjourney AI) as an ink sketch with yellow accents. The text states "Can we "predict" a word?"

Last week, our students learned how to conduct a proper evaluation for an NLP experiment. To this end, we introduced a small textcorpus with sentences about Joseph Fourier, who counts as one of the discoverers of the greenhouse effect, responsible for global warming.

https://github.com/ISE-FIZKarlsruhe/ISE-teaching/blob/b72690d38911b37748082256b61f96cf86171ace/materials/dataset/fouriercorpus.txt

#ise2025 #nlp #lecture #climatechange #globalwarming #historyofscience #climate @fiz_karlsruhe @fizise @tabea @enorouzi @sourisnumerique

#ISE2025

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