teleren/devto huggingface dataset:
NeoGPT PR merge achieved
Tags: python, opensource, programming, beginners
Published: 2024-01-12 12:58:29

I have worked my way through the scope of contributions I set out to make with the NeoGPT open source project.  It has been a positive experience and went a great deal smoother than some past contribution work.  This is primarily due to the authors engaging in the conversations around the issues posted, giving good feedback and also thoroughly reviewing changes and adding their preferences to them.  This has been a surprisingly uncommon experience in my short time as an open source contributor, but I am hoping this is what I encounter more often than not in working as a community.

The 2 functions I originally setout to add to this project have both been merged into the main program and can be found in the [config.py module](https://github.com/neokd/NeoGPT/blob/main/neogpt/config.py).  These functions allow users to export and import their program configuration settings to/from .yaml files.   

As these settings exist in a series of global variables in the config.py file, exporting the settings was as simple as mapping the variables into an “config” object, formatted such that could be passed to the yaml.dump() function which would write the config object to a YAML file.  I have found that Python has a great deal of libraries and modules available that can provide a lot of functionality that I may have previously done manually.  In this case, importing the yaml and toml modules made light work of writing and reading from files.  As well, the argparse module was used in this project which is one of the most helpful library tools I’ve run across.  It makes light work of processing command line arguments at runtime.

The import function also used the yaml module, this time yaml.safe_load() function.  This was used to read an existing yaml configuration file and load its specific values into the current settings to run the program.  So similar to the export process, the object that was created using yaml.safe_load() was then mapped back to populate the global variables that represent the programs configuration settings.  This turned out to be a much easier process than I realized in the beginning.  

As an addition, to the scope of work I originally planned to complete, I decided that providing some unit tests for my work, would help in developing a more complete contribution process. No public facing testing code had yet been added to this projects repo, so perhaps, unit tests for these functions would be a good jumping off point to make the creation of unit tests an ongoing step in the integration of new features and contributions.  Time will tell if that is the case, but I’m very happy with how my work has added to this project to date and it is very rewarding to see some substantial code contributions merged into the main project.
Aria's CSV Parser: Bridging the Gap in C for CSV File Parsing
Tags: c, opensource, programming, development
Published: 2024-01-12 12:32:37

Hello!

I'm excited to share a small and simple project that I just published on my [GitHub](https://github.com/ariasdiniz/csv_parser) – **Aria's CSV Parser**. As a developer often working with CSV data in C, I've always felt the absence of a native CSV parser in the C standard library. This led me to develop Aria's CSV Parser, an open-source, efficient solution for parsing CSV files in C.

## Why Aria's CSV Parser?
While C is a powerful language for system programming, it lacks built-in support for parsing CSV files, a common data format. This gap often forces developers to write cumbersome and error-prone code. Aria's CSV Parser is my answer to this challenge – providing a simple, yet effective tool for reading these files.

## Key Features of Aria's CSV Parser
- **Ease of Reading and Parsing**: Effortlessly read lines from CSV files and parse them into fields.
- **Handles Various Separators**: Whether your CSV uses commas, semicolons, or tabs, the parser adapts to different separators.
- **Robust Memory Management**: Designed with a focus on preventing memory leaks, crucial for reliability.
- **Support for Quoted Fields**: Easily handles fields enclosed in quotes, a common occurrence in CSV files.
- **Straightforward Integration**: Just a few steps to clone, include, and compile it into your project.

## Getting Started with Aria's CSV Parser
Here's a sneak peek into how simple it is to use:

**Reading a CSV File:**
```c
#include "csvparser.h"
#include <stdio.h>

int main() {
    FILE *file = fopen("yourfile.csv", "r");
    char *line;
    while ((line = readline(file)) != NULL) {
        printf("%s", line);
        free(line);
    }
    fclose(file);
    return 0;
}
```

**Parsing a Line from a CSV File:**
```c
#include "csvparser.h"

int main() {
    char *csvLine = "Field1,Field2,Field3";
    char **fields = parseline(csvLine, ",");
    // Now, process these fields as needed.
    return 0;
}
```

If you find Aria's CSV Parser useful, don't forget to check it out on [GitHub](https://github.com/ariasdiniz/csv_parser). I know it's a very simple and small project, but I decided to publish it because it helps me a lot saving time when I have to work with CSV data, and I hope it can help you too.

Thanks for reading this post!
How Concrete Sealing Enhances Durability and Aesthetics
Published: 2024-01-12 12:32:35

Concrete surfaces are prone to staining, whether from spills, oils, or other substances. Unsealed concrete can absorb these stains, making them challenging to remove and detracting from the overall appearance. Concrete sealing creates a protective barrier that acts as a shield against stains. Spills can be easily wiped away, and the surface becomes more resistant to the intrusion of oils, chemicals, and other common staining agents. This stain-resistant quality not only maintains the cleanliness of the concrete but also reduces the effort and cost associated with ongoing maintenance.
**Read The Guest blog**:[https://www.hituponviews.com/how-concrete-sealing-enhances-durability-and-aesthetics/](https://www.hituponviews.com/how-concrete-sealing-enhances-durability-and-aesthetics/)
Weekend support service
Published: 2024-01-12 12:20:30

If you need to work on your systems / applications this #weekend, don't feel lonely, we'll be #here and willing to #help you.

#24x7 #24hourservice #24x7support #technicalsupport #support #heretohelp #hereforyou 
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How to Achieve AZ-120 Exam Brilliance with Dumps
Tags: javascript, programming, beginners, react
Published: 2024-01-12 12:20:03

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) Given the critical nature of SAP workloads, passing this exam validates a professional's expertise in optimizing and managing these solutions effectively. Challenges Faced by Aspirants: While the rewards of earning the AZ-120 certification are significant, the journey to success is often fraught with challenges. The exam's comprehensive nature requires a deep understanding of Azure services and their integration with SAP workloads. Aspiring candidates must navigate a vast sea of information, juggle practical experience with theoretical knowledge, and find effective study materials to prepare adequately. Enter Exam Dumps: Exam dumps, a term  [AZ-120 Dumps ](https://dumpsboss.com/microsoft-exam/az-120/
) that may raise ethical concerns in some circles, are essentially collections of questions and answers purportedly extracted from actual exams. In the context of the AZ-120 exam, these dumps provide a simulated experience, allowing candidates to familiarize themselves with the exam format and test their knowledge. The allure of exam dumps lies in their potential to serve as a shortcut to success, offering a glimpse into the types of questions likely to appear on the actual exam. 

Click here for more >>>>>> https://dumpsboss.com/microsoft-exam/az-120/
A Note to Developers: Considerations for Using "Acme" in Japanese Market
Published: 2024-01-12 12:09:46

Hello, Dev.to community! In this post, I'd like to discuss a significant cultural aspect concerning the use of the term "Acme," particularly when your products or content are intended for users in Japan.

## The Global Understanding of "Acme"

"Acme" is a term well-recognized in many parts of the world as representing the pinnacle or peak, often associated with the highest level of quality or achievement. Its popularization can be largely attributed to the fictional "Acme Corporation" from the Looney Tunes cartoons, epitomizing a generic, universal business entity. This term is humorously embedded in Western media and culture.

## The Different Meaning of "Acme" in Japan

In Japan, however, "Acme," when phonetically translated to "アクメ," adopts a distinctly different meaning, closely associated with sexual climax or orgasm. 

![Online dictionary](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9wiue048e4va6j05k2kr.png)

This is particularly evident in google searches within Japan, where "アクメ" predominantly directs to adult-oriented content. This stark difference in interpretation highlights the need for cultural sensitivity and awareness in translation and localization efforts.

![Google Image Search](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qoz7gf8ra4xhr0f7b39g.png)

## A Message from a Japanese Engineer

As a Japanese engineer, I recognize that "Acme" is an innocuous term in many parts of the world. However, I urge those involved in creating or translating content into Japanese to consider its unique cultural connotation. While not advocating for its complete avoidance, sensitivity in its use, especially in professional or educational contexts, is crucial. In Japan, fictional company names are often created by amalgamating names of well-known corporations, providing a culturally appropriate alternative.
Facebook Clone Swiftui & Firebase
Tags: swift, mobile, ios, tutorial
Published: 2024-01-12 12:06:42

One month ago, I launched a YouTube channel where I'm teaching how to create iOS apps using both swiftui and uikit. I will be teaching how to build iOS apps from scratch, I will be also teaching design patterns, answering some iOS interview questions and talking about everything related to iOS development. If you want to take some time to explore my YouTube Channel, you may find something that will be helpful in your learning journey. Here is the link:https://www.youtube.com/@OmarTHAMRI
My latest uploaded tutorial series is to build a Facebook clone using swiftui and firebase.
part 1: https://www.youtube.com/watch?v=Xjnm8QMOniA
Understand the Prim's Algorithm
Published: 2024-01-12 11:49:14

Prim's Algorithm, named after mathematician Vojtěch Jarník and later popularized by computer scientist Robert C. Prim, is a fundamental algorithm in graph theory. This algorithm efficiently finds the minimum spanning tree (MST) of a connected, undirected graph, providing an optimal way to connect all vertices while minimizing the total weight of the tree. In this article, we will explore the intricacies of Prim's Algorithm, its applications, and its step-by-step implementation.

Here is my article on [Prim's Algorithm in easy words ](https://www.javatpoint.com/prim-algorithm)


**What is Prim's Algorithm?
**

Prim's Algorithm is a greedy algorithm that aims to build a minimum spanning tree by selecting the edge with the smallest weight at each step. The minimum spanning tree is a subgraph that includes all vertices of the original graph, forming a tree without any cycles. The primary goal is to minimize the total weight, which is the sum of the weights of the selected edges.

Explanation of Prim's Algorithm:

Initialization:

Choose an arbitrary starting vertex as the initial node of the minimum spanning tree.
Building the Tree:

At each step, select the edge with the smallest weight that connects a vertex in the minimum spanning tree to a vertex outside the tree.
Add this edge and the connected vertex to the minimum spanning tree.
Repeat Until Completion:

Repeat the process until all vertices are included in the minimum spanning tree.
Optimal Solution:

The resulting tree will be acyclic, connected, and have the minimum possible total edge weight for the given graph.
Implementation:

Prim's Algorithm is often implemented using a priority queue, allowing for efficient selection of the minimum-weight edge at each step. This ensures that the algorithm runs in a time complexity proportional to the number of edges and vertices in the graph. The priority queue helps identify the next edge to be added to the minimum spanning tree quickly.

Applications:

Prim's Algorithm has various applications, especially in network design and optimization problems. Some notable use cases include:

Communication network design to minimize cable length or cost.
Designing circuit boards to minimize connection costs.
Constructing efficient transportation networks
Ibuprofeno.py💊| #21: Explica este código Python
Tags: python, beginners, learning, spanish
Published: 2024-01-12 11:40:48

## **<center>Explica este código Python</center>**
#### <center>**Dificultad:** <mark>Intermedio</mark></center>

```py
s1 = {1, 2, 3}
s2 = {4, 2, 6}
s3 = {2, 5, 7}

print((s1 & s2) | s3) # 🤔
```

- A. `{1, 2, 3, 4, 6}`
- B. `{2}`
- C. `{1, 2, 3, 4, 5, 6, 7}`
- D. `{2, 5, 7}`

---

Respuesta en el primer comentario.
Crush Exams with Confidence Using Exam Topics Free
Published: 2024-01-12 11:37:51


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Balancing Innovation and Privacy: Navigating LLM Augmentation with RAG and RA-DIT
Tags: llm, rag, machinelearning, privacy
Published: 2024-01-12 11:27:29

The advancements in Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Dual Instruction Tuning (RA-DIT) are compelling, especially their implications for intellectual property, data privacy and data governance. These generative model augmentation techniques prompt important data security and privacy-related questions in most enterprise settings.

While RAG merges retrieval-based and generative models to improve text retrieval and generation, suitable for tasks like text summarization and content creation (https://spotintelligence.com/2023/10/19/retrieval-augmented-generation-rag/) RA-DIT, on the other hand, adds retrieval capabilities to LLMs through two fine-tuning stages, refining both the model's use of retrieved data and the retriever's relevance (https://ar5iv.labs.arxiv.org/html/2310.01352).

The two methods differ fundamentally. RAG integrates retrieval with generation, potentially raising privacy concerns in cases where sensitive data is involved. RA-DIT focuses on upgrading existing models for better retrieval, possibly offering more control over sensitive data.

Integrating privacy-enhancing technologies (PETs) like differential privacy and homomorphic encryption could transform LLM use in privacy-sensitive areas.

However, selecting the right PET depends on a sound data production and consumption strategy. For example Differential privacy could potentially obscure individual data points in both RAG and RA-DIT processes, while homomorphic encryption would allow for computations on encrypted data, keeping sensitive information secure. 

Other considerations should include how data will be shared externally, how the data will be used internally, the importance of data accuracy, and whether the challenges are related to modeling and analysis or software engineering. Data access patterns and user expectations, whether they require static data dumps or API interfacing, could aLos influence the PET application strategy.

While these technologies promise enhanced LLM accuracy and context-rich outputs, balancing technical and ethical considerations is crucial. Differential privacy might reduce accuracy, and homomorphic encryption could increase computational demands.

Understanding how RAG and RA-DIT, coupled with PETs, can create a secure, well governed LLM framework and implementation strategy. This approach could improve LLMs in sensitive sectors and accelerate AI adoption, with data privacy and intellectual property as a foundational design element. The potential of these technologies in enhancing auditability, explainability, and governance in LLMs, particularly when privacy is central, is immense.
Gree Ge
Published: 2024-01-12 11:26:59

Our factory establishment in 2017. we have been at the forefront of manufacturing excellence and specializing in CNC precision machining, pnuematic robot gripper and change system. With a commitment to excellence, we have achieved ISO 9001 and IATF 16949:2016 certifications, ensuring the highest quality standards in our operations.
[aluminium machined parts](https://gree-ge.com/CNC-Machining.html)

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Leaking sensitive data via membership inference attacks on machine learning models
Tags: llm, machinelearning, dataengineering, privacy
Published: 2024-01-12 11:25:30

The paper, ["Membership Inference Attacks against Machine Learning Models,"](https://arxiv.org/abs/1610.05820) shows how easy it is to expose how models can inadvertently leak sensitive information about the data they were trained on.

Leaking via membership inference attacks (essentially allow an adversary to determine if a particular data record was used in the training set of a machine learning model) is another reason why the work in the differential privacy space is so interesting and valuable. It’s extremely common to deal with confidential and sensitive data in most enterprise settings, and the assurance that this data cannot be reverse-engineered or exposed is critical.

The authors of the paper does a nice job walking through multiple experiments and evaluations, demonstrating that machine learning models, especially those that are overfitted, are susceptible to these types of attacks. The authors show that the models behave differently when queried with data they have seen before, compared to unseen data. This difference in behavior can be exploited to infer membership information.

A key contribution of the paper is the introduction of a novel technique called "shadow training." This technique involves training models (referred to as shadow models) to mimic the behavior of the target model, using data that is similar to the target model’s training data. The shadow models are then used to generate a dataset for training an attack model, which learns to distinguish between the target model’s outputs on its training and test data. This attack model can then be used to infer membership information about new data records.

The implications of this are far-reaching. Whenever the usefulness of privacy enhancing technologies comes up, it’s appropriate to raise the risks associated with membership inference attacks and take steps to mitigate these risks. This includes being mindful of the trade-offs between model accuracy and vulnerability to such attacks, and implementing strategies to prevent overfitting.

Some questions that arise from this research, and that I believe warrant further exploration, include:

1. How can we effectively measure the susceptibility of our models to membership inference attacks?

2. What are the best practices for implementing shadow training in a real-world scenario, and how can we ensure its effectiveness?

3. Are there specific types of data or model architectures that are more prone to these attacks, and how can we safeguard against this?

I would be interested in engaging with more researchers who are currently delving deeper into these questions and to explore potential collaborative efforts to address these vulnerabilities.
Modélisation d’entreprise : un accélérateur de transformation digitale
Published: 2024-01-12 11:16:18



La transformation digitale est aujourd’hui incontournable pour les entreprises de tous secteurs. Pour relever ce défi, il est essentiel de repenser en profondeur le fonctionnement de l’entreprise : ses processus métiers, ses systèmes d’information, son organisation.

La modélisation d’entreprise apporte une aide précieuse pour conduire ces transformations. En représentant le fonctionnement cible de l’entreprise sous forme de modèles, elle permet de :
🗸 Cartographier les processus métier de bout en bout
🗸 Concevoir le système d’information et les données
🗸 Définir les rôles et responsabilités
🗸 Architecturer les solutions digitales
🗸 Piloter de manière agile les initiatives de transformation

Des outils dédiés pour tirer parti de la modélisation.
De nombreux outils logiciels permettent de réaliser ces modélisations.

On peut citer :
🔹 Des outils open source comme Archi ou Bizagi Modeler pour les premiers cas d’usage de modélisation de processus métiers
🔹 Des suites intégrées comme Mega Hopex ou Bonita qui couvrent à la fois la modélisation et l’exécution des processus
🔹 Des plateformes best-of-breed comme Enterprise Architects qui se focalisent sur les capacités de modélisation d’entreprise.

Zoom sur Enterprise Architect, la référence du marché
Enterprise Architect de SparxSystems fait figure d’outil de référence.

Ses atouts :
➡ Une couverture étendue des différents domaines de modélisation : processus, données, organisation, exigences, logiciels, infrastructure...
➡ L’intégration des standards de modélisation : UML, BPMN, Archimate, MERISE...
➡ Des capacités de simulation, de génération de code et de reverse engineering
➡ Une architecture ouverte et extensible, avec de nombreuses API
➡ Une forte adoption dans les grands comptes et SSII depuis plus de 15 ans

Avec de telles capacités, Enterprise Architects offre une plateforme robuste pour piloter la transformation digitale des entreprises, en couvrant l’ensemble des besoins de modélisation d’entreprise.

Et toi ? Quelles sont tes préférences en matière du choix d'outils de modélisation ? Moi, j'ai adopté Enterprise Architects.
N'hésite pas à partager en commentaire."

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2i4kwcowk1eh8xoe7873.jpeg)