Events Tutorial - Nicola Guarino - FOIS 2024

Event Perception

Perceptions of events accumulate in time.
Perceptions of objects superimpose each other in time.
e.g., I see Jan, I see that he removed the glasses.

Events
- happen in time
- only some proper parts are present whenevery they are present
- all parts are essential
- are uniquely located in spacetime
- do not need a time-idnexed parthood relation
- do not change in time (as a whole)

Quine/Davidson: Events are individuated by the space-temporal reagion where they occur (whatever happens in a spatio-temporal region)
Kim: No, in the same spacio-temporal region, there may be multiple events. On the other hand, Kim does not allow compositionality of events modifiers - warming up and warming up slowly denote two different events.

The most adopted view is still the Davidsonian view, due to the possibility of compositionality.

Facts vs. events
- Events are thick entities, being amenable to be described at different level of detail.
- Facts, on the contrary have a thin nature.
e.g. "The sfere rotates" and "the sfere rotates slowly" are two facts; but you may want to refer to it as the same event.

Bennett's intermediate positions: event descriptions are just names.
- For Bennet, ordinary events are still exemplification of properties, but not of the properties we use to name them.
- They are rather exemplifications of much more complex, unaccessible properties. Event descritptions are just names that, yby picking up some of these properties.
-an event and its description: the coonection just depends on "local context and unpricnipled intuitions"
- Bennett's book: Events and their Names

Is there a systematic relatioship between events and their names (typically verbs)?
Thesis: such core aspects of what happens are based ont he qualitative changes of objects. The systematic relationship between verbs and events ais grounded in qualitative changes.

A different approach: looking inside of events, independenctly of the properties they exemplify.
Question: what is the difference between "the sfere is rotating" and "the sfere is warming up"? The difference is in the quality being considered.

Individuating events
- One way to individuate is by looking at the participants. What are the relevant participants?
- There is a vagueness in trying to individuate minimal participants. e.g. in the Titanic collision, only a part of the boat (the front) and a part of the iceberg were involved in the collision. These parts are the minimal participatns.
- It is helpful to focus on local qualities instead, which are less vague.

Qualities
While properties hold, qualities exist.
Individual qualities survive to change. May be seen variable embodiements of tropes.
A qualitative change echibits a certain variation pattern. When the quality is stable within a certain threshold, we say the quality is unchanged. So this notion also incorporates the notion of unchange.

We can still say events are redefinition of properties but this is not wha an event is..

To summarize:

Complex events and their synchronic structure

- Qualitative changes are the simplest case of events.
- Ordinary events typically have a more complex strcuture, since we tend to cluster together multiple coginitively relevant qualitiative changes.
- Complex events are sums of qualitative changes within them, we distinguish: a) scenes, and
b) cogntively relevant complex events whose individuation criterrie are provided by event kinds (which are cognitive constructions.

Focal event vc. internal context
- focal event - it is what the kind primarily describes.
- internal context - the way the focal event occurs (there are other events involved, besides the focal event).

structure:

JOWO - IFOW - Session 1

What is Food - Adrien Barton

- Context of OBO Foundry
- What do the things we eat have in common?


Part I - Food-related dispositions

1)Edibility - a physical quality inhering in a bearer by virtue of the bearer's disposition of being fit to be consumed. Who is going to consume? Animal, human?
Edibility_for_humans - ... to be consumed by a typical human
Edibiliity_for_P1 (P1 may be related to a particular health condition or intolerance)

Coming to and out of existence of edibility
Edibility is a matter of degree and quantity
Partial edibility - e.g. avocado is partially edible, because you must peal it and remove the seed.

2)Nutritiveness - a disposition borne by a material entity that is realized by providing nutrients when being consumed by a typical human.

- Edibility characterizes the absence of negative consequence.
- Nutritiveness characterizes the existence of a positive consequence.

3) Palatability
This is very culture and individual-relative
Palatability depends on the circumstances (perhaps not a typical disposition)

4) Other food related disposition

phsychoactive effect, energetic effect etc.
These food dispositions are all independent from each other (he gives examples)

Part II - Food role

Appropriate food role - a role of a material entity of a type that is generally considered by some community as appropriate for consumption by ingestion in order to fulfill nutritional needs and/or to provide organoleptic experience.
They justify having this as a role (not property) because of the relational nature of this concept.

Reference:
Borghini A. and Piras, N (2020) Food and foods. Toward a definition. Rivista italiana di Filosofia del Linguaggio.
They take the Social-based view of food perspective explained by Borghini and Piras (2020), while not agreeing with the other definitions they provide. Between Input and Output: The Importance of Modelling Transients in Meal Preparation Tasks - Michaela Kümpel

- The goal of this research is to enable robots to prepare meals.
- Towards this,: developed a food cutting knowledge graph (AKR3 2024)
- Robots can use the graph to perform task variations (there is a demo in the website)

The demo shows that different properties from the objects are considered. For example, to "cut a banana", the demo considers that the banana has a peal, so it must be pealed first. Then, the action of slicing is explained step by step: taking a knife, putting the knife up, putting the knife down etc. The live demo also simulates the robot cutting the fruit.

- FoodOn workgroup researchers converting food-related processes into steps
- She claims that "transients" need to be considered, in the sense that in a recipe, before ingredients compose a dough, it becomes a transient while it is being mixed. She explains some logic formulas defining transients.
- Then she presents an ontological model of transients, using an ad-hoc language, exemplifying the model with the example of the dough.

Benefits of the integration of transients
- increased failure handling
- added information abou initial conditions, intermediate transitions and outcomes

It is interesting that the transits have the same parts as the produced object, but it is not the same. It has some properties that the result does not have, for example, the ability to recognize the ingredients that are being mixed.

Nicola: Linguistics does not recognize this, it seems not to be natural. For example, when putting the cake in the oven, it is already referred the cake as such, although it is not yet done.
She claims that for the robot, it is important to understand the objects in the intermediate states are different, so that they still need to do something about them to transform them into the resulting object.

A Food as Medicine Ontology built on Traditional Chinese Medicine Energetics and Food Actions - Neil Sarkar

- In ancient Egypt, the way people eat and relate to food was considered when practicing medicine. In Chinese medicine, depending on what you eat, that can impact your health. The state of the food (heat, consistency) is also considered in Chinese Medicine for understanding the relation to the health issue/condition. This is very different in modern medicine, which tries to fix a problem with medications, instead of trying and understanding why people are getting sick. For instance, some conditions may be avoided or cured by specific types of diets.
- Cool lunches may help sooth your body and relieve stress.
- Greek Four Humours: depending on the food, you may find a balance between these four humours.

Is there any formal framework that can be used to capture this dependence between health and food?

- Zhen Qi in chinese medicine means balance.
- The correspondence in modern medicine for the chinese concept of Zhen Qi is resilence.

It is important not only treat the patient's illness, but support them throughout the way.

He presented a taxonomy starting from Gu Qi (diet):

He also mapped a data repository of Medicinal Food ingredients and connected it to the ontology.

Physical, Organoleptic, and Taxonomical Food Characteristic Analysis for Ontologies and Research - Damion Doole

He is from Genomics and One Health, SFU

His work integrates a myriad of ontologies in food and related areas. The main food ontology, also cited in the previous presentatios is FoodOn.

- Is mouthfeel a esult of a combination of mastication-related senses
- What characteristics do "seeing" and "touching" cover? Texture, color etc.

There is a module on Diets:
- Diet by digestive capacity
- Diet by type of food
- Diet by nutrition composition
- Diet by agricultural treatment
- Prescriptive vs. Descriptive Diet

How Data Analytics and Data Science Fit: A Join Research Methodological Perspective - By Faiza Bukhsh and Maya Daneva

Definition: Design Science is the design and inestigation of artifacts in context.

Design - design/investigate scinticif methods processes, algorithms.
Investigation - investigat from noisy, structure and unstructured data
Artifacts - extract or extrapolate knowledge and insights
Context - apply in context the knowlede grom data across a broad range of application domains.

There are different perspectives to define/use Data Science, depending on your field. See figure below:

The basic phases of Data Analytics can also be seen as the basic phases of Design Science (it really depends on the project how to frame each phase. For instance, for a software, the phases have to do with software development)

CRISP-DM: a method of Data Mining, which proposes a life cycle which is similar to any design project. It comprehends Business Understandingg, Dta Understanding, Data Preparation, Data Modeling, Evaluation and Deployment. It is an iterative and cyclic process. See figure below:

Different roles are responsible for each activity but they should also collaborate and participate in each other activities. Example:

Many times, the problem happens because the data is not well-prepared. We get excited to run our model and train it, but if the data is noisy, not well-prepared, you will never achieve the accuracy that you are looking for. So you should stop doing mindless effort and go back to data preparation. For acquiring data, there are a few possibilities. One solution she made with a hospital is having the data aways on their site on a particular server and then she can access it through a VPN. But for privacy issues, she cannot access the data itself, only the results of the application of algorithms. *This seems like a promising solution There is also another interesting methodology which has the same phases, but what is special about it is that it always loops back to a previous activity. See figure:

There is a very interesting method called SEMMA. Sample, Explore, Modify, Model and Assess. They have a setp-by-step guidance of how to tackle each phase:

She mentions four methodologies for Design Science. Among them, Roel Wieringa's, Paul Johanssen's and Peffer's. let's start with Paul Johanssen's method

Paul's model has a lot of cycles on it. The Data Science methods also have cylces. So we can start analyzing in which parts of one model we can insert the other. Peffers Design Cycle

Wieringa's method:

In the work of Wieringa, data analysis will be in the treatment design, since this is where you do the modeling, the training and the tunning. But if you are going for a knowledge problem, then it means that you are trying to extract knowledge from knowledge (so something like LLM). Then, data analysis is in the setup phase (for LLM, it will be prompt engineering). How can I know what my artifact is? (the artifact that should be designed)
It all depends on the objective. You have to ask yourself what is the goal of that design project.

What is my artifact?
The artifact in a Data Analytics project can be the Data preparation process itself, it can be the model or the model result, it can be the evaluation process or evaluation criteria. So we have to ask ourselves again what the objective is. And then you will know what is your artifact.

Keynote by Carlos Ribas (Bosch) - The power of Information Systems shaping the future of the Automative Industry

He presented dan interesting tool called Bmlp associated with an operating system named TOM to automate smart factories. Read about it here: https://www.iotm2mcouncil.org/iot-library/news/connected-industries-news/bosch-commits-to-global-industrial-aiot/

He also discussed how Bosch inveted in Digital Twins to help having more prompt predictions of failures in their factories. Read about it here: https://www.bosch-connected-industry.com/de/en/iiot-insights/digital-twins

He talked about the role of AI in Manufacturing

Examples of use:

He also mentioned that none of this is important if tecnhology does not improve the life or work of people who work in the factories.

Digital twins can help simulate in the lab before the equipment is put in the plant. The other use is in real-time data acquisition. For example, when the equipment is put to test, at the same time, they can inspect data coming from the test and discover on the flight. And they will know that in a particular component, under specific conditions, they have errors. This is really helpful for them.

They treat data integration in these terms: from each sensor, data is sent to data repositories in specific format and also adding labels. This facilitates recover data from different apps, different systems. *It seems to me they treat this in the syntactic level.

I also found a link to an interesting data platform: https://www.bosch-connected-industry.com/de/en/portfolio/bosch-semantic-stack
I wonder if there are some more sophisticated semantic technologies in place, of which perhaps Carlos is not aware.

They can detect a problem in the process, not after the process is finished. That is why they feel so much in control. The faulty components are immediately rejected, removed from the process, suffer maintenance, and then go back to the process.
They are not currently investing in LLM because they do not feel the need. Sometimes the volume of data being too high, it does not help.

The people who used to work in the plant doing mechanical work are still there, and they are trained and "re-skilled". In the last years, the process of training has been very intensive. Sometimes, they are not learning new things very easily, but Bosch sees this is a mission. If they do not

People need to develop different competencies. In the future, in the recruitment process, new people need to come with a degree. Currently, many of them are low level engineers (now the work force is 40% of people have at least a degree). They need to gain knowledge about the new technologies. This is a must!

Alessandro Oltramani, an expert in logic-symbolic reasoning is the new leader of the Carnegie Bosch Institute: https://carnegiebosch.cmu.edu/

Giancarlo asked if this shows that such kind of approach is a current bet of Bosch. Carlos responded that is for sure.

What are the Ontological Foundations of Simulation Modeling - Gerd Wagner - SCS Weekly Meeting

What are the Ontological Foundations of Simulation Modeling In simulation modeling, you care about modeling objects and events, since we want to simulate the real world, and these are the most important types of ontological categories in the world. Modeling and simulation (M$S) is concerned with modeling dynamical systems which considt of ojbect that are subject to state changes over tim. This happens when one or more of its attribute values are changed. These attributes that change are called state variables. Attribute values may be continuous (smooth) or discrete (in jumps), leading to continuous or discrete processes. In ISs we are typically more concerned with discrete processes. Sometimes, a mix of them: discrete events (e.g. a car bumping into another in traffic) but also continuous events (movement of different objects in traffic). Discrete Systems: - Example: predator-prey ecosystem such as an area populated by wolves and sheep, where births, death and predator-prey encounters are events. - A discrete dynamical system (such as the one in the example above) can be captured either more abstractly with the help of a continous simulation as in System Dynamics, or iwth the help of a Discrete Event Simulation model. Discrete Evnet Simulation (DES) Paradigms: - Event-based simulation with SIMSCRIPT (1962), Event Graphs (1983) - Process Network simulation with GPSS (1961), Arena (1992), AnyLogic etc. It is based on more high-level concepts w.r.t events, which help you to capture concepts of different domains (e.g. manufacturing, traffic etc.) - Coroutine-based Process Interaction simulation with Simla (1967), SimPy, etc. Coroutines are asynchronous programming process stations, which may start, be interrupted and then reestablish processing. - Simulation based on Petri Nets (from the 60s) Object Event M&S Based on the ontological principles: - objects participate in events - events cause state changes of participating objects and follow-up events according to causal regularities. The sturcture of objects and events is described in the form of a UMLclass mode defining object types and event types. The system's dynamics is described in the forms of DPMN (similar to BPMN) process model defining a set of rules. - which caputre causal regularities (as event rules) - and correspond to transition functions of an Abstract State Machine. Causal Regularity Simple Model:

Example of Object Event Model about Phishing We may see an OE Class Model to model the information and a BPMN/DPMN model to model the events

Agent-based M&S - ontologically speaking, agents are special objets that interact with each other and with their environment. - agents interact with their envionment via a perception-action cycel that is modeled in OEM&S in the form of perception events and action events. - Agents interact with each other by sending and receiving messages. In OEM%S, sending a message is an out-message action event and reeiving a message is an in-message event. Example of a basic BPMN Model of Phishing

Example of a Conceptual Information Model about Phishing

Besides the regular relationships (composition, specialization, and general associations), in these kind of Information Models, there are special kinds of associations and multiplicity restrictions: - the association between the entities mean the participation of agents/objects in events. - the multiplicity can indicate snapshot or historical multiplicity restrictions (you may need both kinds of multiplicity in one model).

Crafting Future Scenarios with the Help of AI - Roland M. Mueller, Katja Thoring at al.

Developing future research poses some problems, including the fact that you don't have the users for manymuch the technology you want to produce. Research questions: Goals: provide AI assistance to future scenario development; AI assistance with scenario rating; AI assisatance with Qualitative Feedback, AI assistance with scenario iterations etc. - Can we democratize access to collective expert knoweldge through Generative AI? - Can we expand the established - Can we build human twins to Project called: Delphi Study Experiment 1: They developed 23 future scenarios using a panel of experts: people from different non-AI fields, such as science fiction authors, business people. And they conmpared that with the ideas of the people in the AI research field. E.g. of solution of the painel of experts: Digital Detox Zone (a place in the office which is not digitally supported) Experiment 2: compare the Human expers and AI experts with a Digital Twin. In short, it does not work yet. Paper to read: Designing the Future With the “Delphi Design Sprint”: Introducing a Novel Method for Design Science Research - https://www.researchgate.net/publication/357746370_Designing_the_Future_With_the_Delphi_Design_Sprint_Introducing_a_Novel_Method_for_Design_Science_Research Discussion about the use of Digital Twins in these scenarios: - Good potential for triangulation with field experts and AI people. - Good inspiration for future works in this area Ethical concerns: - GenAI hallucinations are not asuch aproblem for scenairo development compared to factual quesitons - Tranparency of AI involvement - Specific requirement and charactiristic of AI scneaqrio Crafting Future Scnarios with the Help of AI: Potentials of a Hybrid Delphi Expert Panel. HICSS Mind th eFuturee Gap: Introducting the FOD Framework for Future Oriented Design. HICSS

Digital Everything: From Twins to Circular Economy - Barbara Dinter

Digital Everything: From Twins to Circular Economy Barbara Dinter Barbara is one of the IS chairs, focusing on Business Intelligence in TU Chemnitz This presentation is about some german-funded projects. Project 1 - Co-Twin - Vision of a collaboration digital tiwn (DT) in value chain networks. - Whole life cycle - she applies Business Models They transfered the ARIS idea of views (BP view, Data view etc.) to Digital Twins. They have: component view, data view, visualization view, network view... and others. - For all stakeholders in a value chain. The DT is used on the planning phase Results: demonstrator prottoype, 3 use cases, taxanmy, reference architecture, design guidelines and conceptualizations. Project 2 - The circular economy - Part 1 - integration with Co-Twin project Goals: sustainability, enrionmental protection and increased efficiency. Key aspects: Reduce, reuse, repair and recycle; sustainable business models, systemic approach, design for longevity and integration of digital technologies. - Part 2 - The circular economy Digital ecosytem for circular economy in the automotive industry (DIONA) collaboration with other academic partners: TU Dortmun and Fraunshofer ISST. She also mentioned 12 projects with industry. DIONA Focus areas - Transfer and networking: coordination of 12 MobilKreis projects, oraganization of physical and digital meetings, knwoeldge tranfer research activities. - Cyberphysical Lab for SMEs to open experimental space for simulations and test and vailidate scnearios without disrupting live processes. - Digital Hub Research topics: 1) conceptualization of use caes in Circular economy 2) BPM in Circular Economy (adaptation of capabilities, models and technologies for that)