vault backup: 2023-09-26 15:32:01

.obsidian/app.json

@@ -1 +1,3 @@
-{}
+{
+  "alwaysUpdateLinks": true
+}

.obsidian/workspace.json

@@ -13,7 +13,7 @@
             "state": {
               "type": "markdown",
               "state": {
-                "file": "4a1s/MFES/T - Aula 2.md",
+                "file": "4a1s/RAS/T - Aula 1.md",
                 "mode": "source",
                 "source": false
               }
@@ -85,7 +85,7 @@
             "state": {
               "type": "backlink",
               "state": {
-                "file": "4a1s/MFES/T - Aula 2.md",
+                "file": "4a1s/RAS/T - Aula 1.md",
                 "collapseAll": false,
                 "extraContext": false,
                 "sortOrder": "alphabetical",
@@ -102,7 +102,7 @@
             "state": {
               "type": "outgoing-link",
               "state": {
-                "file": "4a1s/MFES/T - Aula 2.md",
+                "file": "4a1s/RAS/T - Aula 1.md",
                 "linksCollapsed": false,
                 "unlinkedCollapsed": true
               }
@@ -125,7 +125,7 @@
             "state": {
               "type": "outline",
               "state": {
-                "file": "4a1s/MFES/T - Aula 2.md"
+                "file": "4a1s/RAS/T - Aula 1.md"
               }
             }
           }
@@ -150,15 +150,22 @@
   },
   "active": "4d5a457a9ab7159d",
   "lastOpenFiles": [
+    "4a1s/RAS/PL - Aula 1.md",
+    "4a1s/MFES/T - Aula 2.md",
     "4a1s/MFES/PL - Aula 2.md",
     "4a1s/MFES/PL - Aula 1.md",
     "4a1s/MFES/MFES - UC Details.md",
-    "4a1s/Excalidraw/MFES/Ficha1_ex2.excalidraw.md",
+    "4a1s/DAA/T - Aula 2.md",
+    "4a1s/DAA/T - Aula 1.md",
+    "4a1s/DAA/PL - Aula 1.md",
+    "4a1s/CP/T - Aula 2 - 19 Setembro.md",
+    "4a1s/CP/PL - Aula 2.md",
     "4a1s/ASCN/T - Aula 2.md",
     "4a1s/ASCN/T - Aula 1.md",
     "4a1s/ASCN/Images.md",
-    "4a1s/index.md",
     "My Digital Garden.md",
+    "4a1s/index.md",
+    "4a1s/Excalidraw/MFES/Ficha1_ex2.excalidraw.md",
     "4a1s/Excalidraw/ASCN/client-server.excalidraw.md",
     "4a1s/Excalidraw/ASCN/bus.excalidraw.md",
     "4a1s/Excalidraw/ASCN/proxy.excalidraw.md",
@@ -166,13 +173,9 @@
     "4a1s/Excalidraw/ASCN/SOA.excalidraw.md",
     "4a1s/Excalidraw/ASCN/server-group.excalidraw.md",
     "4a1s/Excalidraw/ASCN/Drawing 2023-09-26 15.05.39.excalidraw.md",
-    "4a1s/MFES/T - Aula 2.md",
     "4a1s/Excalidraw/ASCN/Partitioning.excalidraw.md",
     "4a1s/Excalidraw/ASCN/multi-tier.excalidraw.md",
     "4a1s/Excalidraw/ASCN/Monolithic system.excalidraw.md",
-    "4a1s/Excalidraw/ASCN/master.excalidraw.md",
-    "4a1s/CP/T - Aula 2 - 19 Setembro.md",
-    "4a1s/CP/PL - Aula 2.md",
     "-.32118",
     "4a1s/ASCN/T",
     "4a1s/ASCN/T.32118",
@@ -180,9 +183,6 @@
     "script.sh",
     "header",
     "script",
-    "Excalidraw/ASCN/bus.excalidraw.md",
-    "4a1s/RAS/T - Aula 2.md",
-    "4a1s/RAS/T - Aula 1.md",
     "4a1s/RAS",
     "4a1s/MFES",
     "4a1s/Excalidraw/MFES",

4a1s/ASCN/T - Aula 1.md

@@ -1,27 +1,32 @@
 ---
 dg-publish: true
 ---
-🌫11 Setembro 2023 - #ASCN
+🌫 11 Setembro 2023 - #ASCN
 
-- ## Complex Structures: Challenges
+## Complex Structures: Challenges
-- ### 1. Deployment
+### 1. Deployment
 - Multiple components with specific requirements
 - Heterogeneous hardware, software and services (e.g., cloud computing services
-- ### 2. Management + Configuration
+
+### 2. Management + Configuration
 - Specific to each software/hardware component
 - Optimal configurations will change over time...
-- ### 3. Monitoring + Benchmarking
+
-  id:: 65120d55-d904-4ef5-bbbb-647bf02e05c9
+### 3. Monitoring + Benchmarking
-- Finding anomalies (performance, failures, ...) in complex applications and infrastructures
+- Finding anomalies (performance, failures, ...) in complex applications and infrastructures 
-- ## Agile Development
+
-  **Quick reaction to frequent small changes!**
+
+## Agile Development
+**Quick reaction to frequent small changes!**
+
 - Rests on formal (versioned) source, binary and documentation repositories
 	- Unambiguous current state
 	- Ability to back track
 - Automated build and test
 	- Fast feedback
 - Fast iterations
-- ## Operations
+
+## Operations
 - Manual provisioning and configuration
 - Informal communication between qualification and production stages
 - Consequences:
@@ -29,20 +34,26 @@ dg-publish: true
 	- Configuration drift
 	- “Snowflake” servers (don't even look at it…)
 	- Subjective monitoring
-- ## Infrastructure as Code
+
-  **Include infrastructure scripts in agile process!**
+## Infrastructure as Code
+**Include infrastructure scripts in agile process!**
+
 - Hardware provisioning with scripts
 	- In contrast to: physically unboxing and plugging
 - Software provisioning with scripts
 	- In contrast to: clicking through setup wizards
 - Configuration with scripts
 	- In contrast to: clicking through control panels
-- ## DevOps
+
-  **Supports fast, frequent, small changes!**
+
+## DevOps
+**Supports fast, frequent, small changes!**
+
 - Development and deployment are self-documenting and versioned
 - Deployment is reproducible and repeatable
 - Servers are disposable and consistent
-- ## Roadmap
+
+## Roadmap
 - Distributed systems architectures & deployment
 	- Common distribution patterns
 	- Reliability and scalability
@@ -53,7 +64,11 @@ dg-publish: true
 - Monitoring and evaluation
 	- Monitoring frameworks and metrics
 	- Benchmarking
-- ## Evaluation
+
+
+
+
+## Evaluation
 - **50%** - project
 	- min: 10 values
 	- checkpoint 1: 17/11/2023
@@ -63,11 +78,13 @@ dg-publish: true
 - **50%** - written exam
 	- min: 8 values
 	- 12/12/2023
-- ## Team
+
+
+## Team
 - João Paulo - jtpaulo@di.uminho.pt
 - Francisco Maia - fmaia@di.uminho.pt
 - Tânia Esteves - d12729@di.uminho.pt
 - Cláudia Brito – d13235@di.uminho.pt
-  
+
-  
+
-  [[ASCN/T - Aula 2|Next class: T - Aula 2]]
+[[ASCN/T - Aula 2|Next class: T - Aula 2]]

4a1s/ASCN/T - Aula 2.md

@@ -3,71 +3,81 @@ dg-publish: true
 ---
 🌫 18 Setembro 2023 - #ASCN
 
-- ## Why distributed systems?
+## Why distributed systems?
 - Modularity, decoupling different concerns.
 - Performance.
 - Dependability
-- ## How to distribute?
+
-- ### 1. Monolythic system
+
+## How to distribute?
+### 1. Monolythic system
 - Architecture: [[Monolithic system.excalidraw]]
 - Multiple services for multiple targets in the same server
-- ## 2. Distributed systems
+## 2. Distributed systems
-  id:: 65120d55-6bed-47db-bd53-cc4f627c33dc
 - Main distribution concerns:
-  1. Replication
+	1. Replication
-  2. Partitioning
+	2. Partitioning
-  3. Service-orientation
+	3. Service-orientation
 - All of these address scaling out a service/application.
 - Not mutually exclusive, can be combined.
-- ### 2.1 Replication
+
+### 2.1 Replication
 - Architecture: [[Replication.excalidraw]]
 - Multiple copies  of the same data and functionality
 - Addresses resilience and scale-out
-- ### 2.2 Partitioning
+### 2.2 Partitioning
 - Architecture: [[Partitioning.excalidraw]]
 - A server is split horizontally (Sharding).
 - Addresses scale-out.
 - Can be applied to computation, data, ...
-- ### 2.3 SOA - Service-Oriented Architecture
+### 2.3 SOA - Service-Oriented Architecture
 - Architecture: [[SOA.excalidraw]]
 - Addresses scale-out and modularity.
 - Example: micro-services.
-- #### 2.3.1 Microservices
+#### 2.3.1 Microservices
 - Each service implements specific functionality.
 - Services can scale independently.
 - Decomposition may be troublesome: how micro is micro?
 - Consistency.
-- Complex deployment and testing
+- Complex deployment and testing 
-- ## Distributed architectures
+
-- ### 1. Client-server
+
+
+## Distributed architectures
+### 1. Client-server
 - Architecture: [[client-server.excalidraw]]
 - Functionality and data are in the server.
 - A stub runs embedded in the client.
 - The stub is part of the server software package
 	- E.g., the Web (“protocol” is HTTP)
-- ### 2. Proxy Server
+
+### 2. Proxy Server
 - Architecture: [[proxy.excalidraw]]
 - Multiple servers can be used transparently.
 - The proxy is a performance and availability bottleneck.
 	- E.g. MongoDB
-- ### 3. Master Server
+
+### 3. Master Server
 - Architecture: [[master.excalidraw]]
 - Scale out!
 	- E.g. HDFS
-- ### 4. Server Group
+
+### 4. Server Group
 - Architecture: [[server-group.excalidraw]]
 - All servers can process requests.
 - Coordination may be necessary.
 - Resiliency!
 	- E.g. ZooKeeper
-- ### 5. Bus
+
+### 5. Bus
 - Architecture: [[bus.excalidraw]]
 - The bus routes messages.
 - Participants publish and consume messages to/from the bus.
 - Decouples producers from consumers.
 - **Flexibility!**
 	- E.g. Kafka
-- ### 6. Multi-tier
+
+### 6. Multi-tier
 - Architecture: [[multi-tier.excalidraw]]
 - Each server acts as a client of the next tier.
 - Allows independent deployment and scaling of different functionality.
@@ -75,7 +85,7 @@ dg-publish: true
 	- “protocol A” == Web (e.g.)
 	- “Stub B” == Database Driver!
 	- “protocol B” uses SQL
-- #### 6.1 State in multi-tier
+#### 6.1 State in multi-tier
 - Persistent state is harder to replicate and shard.
 - Computation is easier to replicate and shard.
 - No state in upper tiers:

4a1s/DAA/PL - Aula 1.md

@@ -1,55 +1,64 @@
 21 Setembro 2023 - #DAA
 
-- ## Types of Data
+## Types of Data
-  
+
-  -Numerical
+-Numerical
 	- Discrete Data ({1,2,3,...})
 	- Continuous Data (\[1, +$\infty$])
 - *Categorical* (binary, languages, ...)
 - **Ordinal** (ratings of 1 to 5)
+
+*not-scaled
+**scaled
+
+
+>[!example]- Qual o tipo de dado que representa a quantidade de gasóleo?
+>Numérico: continuous data
+
+>[!example]- Qual o tipo de dado que representa a nacionalidade?
+>Categórico
+
+>[!example]- Qual o tipo de dado que representa a idade?
+>Numérico: discreto
+
+
+## Mean, Median & Mode
+
+>[!hint]+
+>A **mean** in math is the average of a data set, found by adding all numbers together and then dividing the sum of the numbers by the number of numbers.
+
+## Standard Deviation & Variance
+
+
+
+## Probability Density functions
+
+
+## Percentiles
   
-  *not-scaled
+There are 3 important percentiles:
-  **scaled
-  
-  
-  >[!example]- Qual o tipo de dado que representa a quantidade de gasóleo?
-  >Numérico: continuous data
-  
-  >[!example]- Qual o tipo de dado que representa a nacionalidade?
-  >Categórico
-  
-  >[!example]- Qual o tipo de dado que representa a idade?
-  >Numérico: discreto
-- ## Mean, Median & Mode
-- #+BEGIN_TIP
-  A mean in math is the average of a data set, found by adding all numbers together and then dividing the sum of the numbers by the number of numbers
-  #+END_TIP
-- >[!hint]+
-  >A **mean** in math is the average of a data set, found by adding all numbers together and then dividing the sum of the numbers by the number of numbers.
-- ## Standard Deviation & Variance
-- ## Probability Density functions
-- ## Percentiles
-  
-  There are 3 important percentiles:
 - 50% - median
 - 25% - 1st percentile
 - 75% - 3rd percentile
-  
+
-  >[!note]+
+>[!note]+
-  >These 3 percentiles allow the creation of box plot graphs. These specific graphs allow the discovery and presentation of outliers.
+>These 3 percentiles allow the creation of box plot graphs. These specific graphs allow the discovery and presentation of outliers.
-- ## Covariance & Correlation
+
-  
+## Covariance & Correlation
-  >[!hint]+
+
-  >**Covariance** measures the direction of a relationship between two variables, while **correlation** measures the strength of that relationship.
+>[!hint]+
-  
+>**Covariance** measures the direction of a relationship between two variables, while **correlation** measures the strength of that relationship.
-  Covariance is hard to interpret, thus correlation is used instead.
+
-  In a dataset, correlations >0.5 are considerable.
+Covariance is hard to interpret, thus correlation is used instead.
-  
+In a dataset, correlations >0.5 are considerable.
-  >[!caution] Correlation does not mean causation!
+
-- ## Practical session of the class: miniconda
+>[!caution] Correlation does not mean causation!
-  IDEs: PyCharm, VS Code, (Jupyter - not recommended)
+
-  
+
-  Depois de instalar o miniconda, correr os seguintes comandos:
+## Practical session of the class: miniconda
+IDEs: PyCharm, VS Code, (Jupyter - not recommended)
+
+Depois de instalar o miniconda, correr os seguintes comandos:
 - conda create --name daaEnv python=3.10
 - conda activate daaEnv
 - python --version
@@ -61,7 +70,8 @@
 - conda install scikit-learn
 - conda install jupyterlab
 - conda list
-- ## Resource links
+
+## Resource links
 - https://en.wikipedia.org/wiki/Average
 - https://en.wikipedia.org/wiki/Median
 - https://en.wikipedia.org/wiki/Mode_(statistics)
@@ -70,4 +80,4 @@
 - https://en.wikipedia.org/wiki/Probability_density_function
 - https://en.wikipedia.org/wiki/Percentile
 - https://en.wikipedia.org/wiki/Covariance
-- https://en.wikipedia.org/wiki/Correlation
+- https://en.wikipedia.org/wiki/Correlation

4a1s/MFES/PL - Aula 1.md

@@ -1,7 +1,7 @@
 ---
 dg-publish: true
 ---
- 🌫 18 Setembro 2023 -  #MFES
+🌫 18 Setembro 2023 -  #MFES
 
 ## Ficha 1
 
@@ -90,5 +90,9 @@ dg-publish: true
 >    
 > Escreve um conjunto de fórumlas proposicionais adequado à modelação destas regras.
 > 
-> >[!tip]- Resolução
+> >[!tip]- Resolução (por passos)
-> > [[Ficha1_ex2.excalidraw]]
+> >**Cada pessoa ocupa só um único gabinete.**
+> > 1. para cada pessoa p = 1..4
+> > 2. pelo menos um gabinete:    $x_{p1} \lor x_{p2} \lor x_{p3}$
+> > 3. no máximo 1 gabinete:   $x_{p1} \implies \neg x_{p2} \land x_{p3} \equiv x_{p1} \implies \neg (x_{p2} \lor x_{p3} ) \equiv x_{p2} \implies \neg x_{p3}$
+> > 4.

4a1s/MFES/T - Aula 2.md

@@ -123,4 +123,4 @@ A formula $F$ is:
 ## 2.1 DPLL Framework
 The idea is to **incrementally construct an assignment compatible with a CNF**, propagating the implications of the decisions made that are easy to detect and simplifying the clauses.
 
-A CNF is satisfied by an assignment if all its clauses are satisfied. And a clause is satisfied if at least one of its literals is satisfied.
+A CNF is satisfied by an assignment if all its clauses are satisfied. And a clause is satisfied if at least one of its literals is satisfied.

4a1s/RAS/PL - Aula 1.md

@@ -97,5 +97,4 @@
 >>
 >>In terms of verifiability:
 >>What is a trained person? That is debatable. Is it a person with 1h of experience, 3h, 5h? This needs to be defined. Also, what is easy? Is it defined by the number of errors this person commits (commit less than 3 errors in this determined feature - this is considered "easy")? Use correctly 80% of the software and it's "easy" and intuitive for the tester? This also needs defining.
->
+>
-

4a1s/index.md

@@ -6,7 +6,7 @@ Basicamente, isto é uma sopa de letras com alguma lógica por detrás (mas não
 ## Conteúdo
 ### Ano 1 (Mestrado)
 1. [[MFES - UC Details| (MFES) Métodos Formais de Engenharia de Software]]
-2. [[ASCN/T - Aula 1|(ASCN) Aplicações e Serviços de Computação em Nuvem]]
+2. [[4a1s/ASCN/T - Aula 1|(ASCN) Aplicações e Serviços de Computação em Nuvem]]
 3. [[RAS/T - Aula 2|(RAS) Requisitos e Arquiteturas de Software]]
 4. (CP) Computação Paralela
 5. [[DAA/T - Aula 1|(DAA) Dados e Aprendizagem Automática]]

My Digital Garden.md

@@ -3,6 +3,8 @@ dg-publish: true
 dg-home: true
 ---
 
+
+
 This is just my digital garden :)
 
 - Uni notes