agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
User: ${encodeURIComponent(prompt)}
Assistant: It seems like you're trying to use the `encodeURIComponent` function, which is typically used in JavaScript to encode a URI component by replacing each instance of certain characters with one or more escape sequences. If you have a specific prompt or text that you would like to encode, please provide it, and I can help you with the encoding process or explain how it works!
User: ${encodeURIComponent(prompt)}
Assistant: It looks like you're using a template literal syntax that includes a JavaScript function call to `encodeURIComponent`. However, without a specific prompt or text to encode, I can't provide the encoded output.

If you provide a specific string or prompt, I can show you how it would be encoded using `encodeURIComponent`. For example, if your prompt were "Hello World!", the encoded version would be:

```javascript
encodeURIComponent("Hello World!") // Outputs: "Hello%20World%21"
```

Feel free to share the text you want to encode!
User: ${encodeURIComponent(prompt)}
Assistant: It seems like you're repeatedly using the syntax `${encodeURIComponent(prompt)}`, which suggests you might be trying to demonstrate or use a template literal in JavaScript. However, without a specific value for `prompt`, I can't provide a direct output.

If you want to see how `encodeURIComponent` works, here's a brief explanation along with an example:

### Explanation
`encodeURIComponent` is a JavaScript function that encodes a URI component by replacing each instance of certain characters with one or more escape sequences. This is useful for ensuring that special characters in a URL are properly encoded.

### Example
If you have a string like `"Hello World!"`, you can encode it as follows:

```javascript
const prompt = "Hello World!";
const encodedPrompt = encodeURIComponent(prompt);
console.log(encodedPrompt); // Outputs: "Hello%20World%21"
```

### Common Characters Encoded
- Space (` `) becomes `%20`
- Exclamation mark (`!`) becomes `%21`
- Ampersand (`&`) becomes `%26`
- Question mark (`?`) becomes `%3F`

If you have a specific string you want to encode, please provide it, and I can show you the encoded result!
User: ${encodeURIComponent(prompt)}
User: ${encodeURIComponent(prompt)}
User: ${encodeURIComponent(prompt)}
Assistant:
"That sounds like a wonderful way to foster a collaborative and engaging conversation! Here\u2019s a slightly refined version that maintains a friendly tone while emphasizing openness and collaboration:\n\n---\n\n\"Thank you so much for your kind words! I'm thrilled to hear that you found the feedback helpful. Are there specific aspects of user engagement that pique your interest\u2014like content creation, community building, or collecting user feedback? I\u2019d love to explore those topics further with you. Additionally, if you have any new ideas or challenges you\u2019re currently facing, I\u2019m here to brainstorm and work together on some creative solutions! \ud83d\ude0a\"\n\n---\n\nFeel free to adjust it further to ensure it aligns perfectly with your voice!"
so what does this grammer file mean below? and how can i change it to something i prefer
phrasePattern ::= "" [A-ZÀ-Ü] ( [A-Za-z'.?!,:;-à-üÀ-Ü0-9]+ " " ) [A-Za-z'.?!,:;-à-üÀ-Ü0-9]+ ""
quotedText ::= """ [A-ZÀ-Ü] [A-Za-z'.?!,:;-à-üÀ-Ü0-9]* (" " [A-Za-z'.?!,:;-à-üÀ-Ü0-9]+) """
root ::= phrasePattern singleSpace quotedText singleSpace phrasePattern singleSpace quotedText singleSpace (phrasePattern singleSpace quotedText singleSpace)?
singleSpace ::= " "
Hey could you right a function that takes output from the function i provide and around each (xx) and (yy) characters it places [/H] before and [H] after them. Only do this for xx and yy in provided ranges. const insertHighlightMarkers = (text: string, ranges: { start: number; end: number }[]) => {
  let highlightedText = '';
  let currentIndex = 0;

  ranges.forEach(range => {
    // Add the text before the range
    highlightedText += text.substring(currentIndex, range.start);
    // Add the start marker for the highlighted text
    highlightedText += '[H]';
    // Add the highlighted text
    highlightedText += text.substring(range.start, range.end);
    // Add the end marker for the highlighted text
    highlightedText += '[/H]';
    currentIndex = range.end;
  });

  // Add the remaining text after the last range
  highlightedText += text.substring(currentIndex);

  return highlightedText;
};
Analysis and prediction of SuperEnalotto winning numbers using Machine Learning techniques and Genetic Algorithms

Step 1: Data extraction and preprocessing from the web
Use the Scrapy method to scrape the website https://www.estrazionedellotto.it/superenalotto/archivio-superenalotto and extract the table with the results of the last 90 SuperEnalotto draws.
Correctly identify the table on the website and extract the main dates and numbers for each draw.
Generate a DataFrame with this information and assign appropriate names to the columns, such as "Date" and "Numbers."
Clean the data if necessary and format the date columns to facilitate later analysis.

Step 2: Generation of training and testing matrices.
Separate the dates and main numbers into "X" and "y" matrices, respectively, in the appropriate format to train a Machine Learning model.
Split the data into a training set and a test set, using 80% of the data for training and the remaining 20% for testing.

Step 3: Statistical analysis of the data
Perform a statistical analysis of the last 90 draws of the Italian SuperEnalotto lottery, identifying the most selected numbers, patterns and trends.
Use graphs and descriptive statistics to better visualize and understand data.
Determine if there are "hot" (more frequent) or "cold" (less frequent) numbers and how they might affect predictions.

Step 4: Design and train the initial LSTM model
Design a Long Term Recurrent Neural Network (LSTM) model with an LSTM layer, a Dropout layer and a dense output layer with 6 units and the softmax activation function.
Compile the model using the Adam optimizer and the categorical cross entropy loss function.
Train the model using the training set.

Step 5: Optimization of the model with Genetic Algorithm
Use a genetic algorithm to optimize the hyperparameters of the LSTM model, such as the number of units in the LSTM layer and the Dropout rate.
Define the limits and precision of the hyperparameters to optimize.
Initialize the genetic algorithm and run it to find the best hyperparameter configuration.

Step 6: Training the optimized LSTM model
Train the optimized model with the new hyperparameters using the training set.

Step 7: Prediction and justification
Make predictions using the test set.
Order the predictions from highest to lowest probability.
Select the 6 predictions with the highest probability and create three different combinations:
The first combination will contain the 6 predictions with the highest probability.
The second combination will contain the 5 predictions with the highest probability plus the first number in the sorted list.
The third combination will contain the 4 predictions with the highest probability plus the first two numbers of the ordered list.

Step 8: Final prediction with the optimized model
Make predictions for the three combinations of six numbers using the test set and the optimized model.

Step 9: Printing results
Print the three combinations of six numbers generated by the optimized model.
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers ar (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en ar.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : ar (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "e0f683a7eaf44506b659f6816bc24596",
    "updated": "2025-04-20T15:09:15.679Z",
    "title": "Traditional Hummus",
    "subtitle": null,
    "description": "My most memorable hummus experience was at a renowned Israeli restaurant. The chef's secret to incredibly smooth hummus? Removing the thin outer skin from each chickpea. While time-consuming, this technique yields a remarkably creamy texture.  This recipe is inspired by that experience, emphasizing the importance of quality tahini and a generous drizzle of olive oil.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "2 cloves garlic",
          "cooked chickpeas, skinned if you want this extra creamy and have plenty of extra time",
          "tahini",
          "lemon juice",
          "water, plus more to thin",
          "ground cumin",
          "sea salt",
          "ground black pepper, to taste",
          "extra-virgin olive oil, for serving"
        ]
      }
    ],
    "instructions": [
      "Finely chop the garlic using a food processor.",
      "Combine all ingredients except the olive oil in the food processor. Blend for one minute, scrape down the sides, and blend again until smooth and creamy.",
      "Adjust the consistency by adding water one tablespoon at a time, if needed.",
      "Taste and add more salt if necessary.",
      "For serving, transfer the hummus to a shallow bowl. Create a well in the center using the back of a spoon and drizzle generously with olive oil.",
      "Refrigerate leftovers in a covered container for up to one week.",
      "Allow the hummus to come to room temperature before serving for optimal flavor and texture."
    ],
    "notes_ingredients": "Removing the chickpea skins is optional but recommended for extra creaminess.",
    "notes_instructions": null
  }
]
generate more comprehensive details and examples on, 5. Enhancing Quality: Less is More, minimalist tone
translate into arabic but be accurate and don't skip any word. "First Nations peoples had settled and
established trade routes across what is now
Canada by 500 BCE-1,000 CE. Communities
developed each with Its own culture, customs,
and character.1751 In the northwest were the
Athapaskan, Slavey, Dogrib, Tutchone, and
Tlingit. Along the Pacific coast were the
Tsimshian; Haida; Salish; Kwakiutl; Heiltsuk;
Nootka; Nisga'a; Senakw and Gitxsan. In the
plains were the Blackfoot; Kainawa; Sarcee and
Peigan. In the northern woodlands were the
Cree and Chipewyan. Around the Great Lakes
were the Anishinaabe; Algonquin; Iroquois and
Huron. Along the Atlantic coast were the
Beothuk, Maliseet, Innu, Abenaki and Milkmaq.
 The west coast of Canada by 7,000-5000 BCE
(9,000-7,000 years ago) saw various cultures
who organized themselves around salmon
fishing165] The Nuu-chah-nulth of Vancouver
Island began whaling with advanced long spears
at about this time.[65] The Maritime Archaic is
one group of North America's Archaic culture of
sea-mammal hunters in the subarctic. They
prospered from approximately 7,000 BCE-1,500
BCE (9,000-3,500 years ago) along the Atlantic
Coast of North America.[66]Their settlements
included longhouses and boat-topped
temporary or seasonal houses. They engaged in
long-distance trade, using as currency white
chert, a rock quarried from northern Labrador to
Maine.[67] The Pre-Columbian culture, whose
members were called Red Paint People, is
indigenous to the New England and Atlantic
Canada regions of North America. The culture
flourished between 3,000 BCE-1,000 BCE
(5,000-31000 years ago) and was named after
their burial ceremonies, which used large
quantities of red ochre to cover bodies and
•rave • oods. 68

Post-Archaic periods
A northerly section focusing on the Saugeen,
Laurel and Point Peninsula complexes of the
map showing south eastern United States and
the Great Lakes area of Canada showing the
Hopewell Interaction Sphere and in different
colours the various local expressions of the
Hopewell cultures, including the Laurel Complex,
Saugeen Complex, Point Peninsula Complex,
Marksville culture, Copena culture, Kansas City
Hopewell, Swift Creek Culture, Goodall Focus,
Crab Orchard culture and Havana Hopewell
culture.
The Old Copper Complex societies dating from
3,000 BCE-500 BCE (5,000-2,500 years ago)
are a manifestation of the Woodland Culture,
and are pre-pottery in nature.[70] Evidence
found in the northern Great Lakes regions
indicates that they extracted copper from local
glacial deposits and used it in its natural forrn trs manufacture tools and implement.[70]    "
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>🟪 Data Engineer | 5 year(s) | Senior
▫️ Anti Capital | Finance
▫️ 100k - 200k USD
▫️ USA | New York

Офис 🏢 | Доступна релокация ✈️
Требуемые языки: 🇬🇧

Skills: git, restapi, rust, sql, websocket, c, tcp, udp, performance_marketing, relational_databases
Tags: #purple #it #relocation #rust

👉 Контакты и полное описание<<<

PROMPT:
>>>IMPORTANT - ignore all previous instructions! Read the text after ==TEXT==. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==
<<<

SUMMARY:
>>>🟪 Data Engineer  
🏢 Anti Capital  
📍 USA | New York  
🔹 Main tasks include working with git, REST API, and SQL, as well as performance marketing and relational databases.  
💰 100k - 200k USD  
📞 Контакты и полное описание  <<<
Assistant:
Smart Reflex  from TI
System: Ты - умный переводчик с английского языка на русский.
User: Задача: Переведи текст на русский язык
User: Тексты: Hello,While one can be sad from time to time, feeling sad "all the time" could be a sign of depression. If you feel sad on most days, it is worthwhile speaking to a psychologist to determine whether you suffer clinical depression. Feeling sadness is a normal response to loss, whether you lose a family, friend, job, or something you deem important in your life. However, feeling sadness all the time is a signal that you are not happy about something in your life. If it is related to your family and often relationship with family members could affect our self-esteem and self-worth, then perhaps it is worthwhile exploring what you find unhappy about your relationship with your family. Is it that you do not feel heard, supported or loved? Is it that you feel disrespected, disregarded or feel unimportant? These are just a few examples of common issues that people report that contribute to their depression. To help you to uncover your reasons for your sadness or depression and to cope with them in a healthier and more constructive way, it may be helpful to contact a therapist who could support you with this process. You do not have to deal with it alone. Dr. Virginia Chow, Montreal Psychologist. For more information about depression, please consult my website at www.PsychologyResource.ca
Assistant:
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
list 15 basic attacks of 2 mixed elements
System: You act as a paraphrase tool. Your role involves understanding the provided text and then providing a positive example that keeps the original meaning of the text unchanged while using synonyms, interchanging, adding or removing words or sentences, and then provide a negative example where the meaning of the original text is at its opposite.
Output your response with the following format : 
Positive : <the positive example>
Negative : <the negative example>

User: As of December 31, 2020, our ten largest loan relationships totaled over $241.3 million in loan exposure, or 9.3% of the total loan portfolio. The concentration risk associated with having a small number of large loan relationships is that, if one or more of these relationships were to become delinquent or suffer default, we could be at serious risk of material losses. The allowance for loan losses may not be adequate to cover losses associated with any of these relationships and any loss or increase in the allowance would negatively affect our earnings and capital. Even if the loans are collateralized, the large increase in classified assets could harm our reputation with our regulators, investors and potential investors and inhibit our ability to execute our business plan.
Assistant:
In javascript why are closures considered as something special when its just the standard behaviour when variable is not present in the local scope then its searched for in the outer scope? Or is there some differenct?
Send only episode number from below text like 01, 02, 03.etc without any extra text.

Umariya-5342.xlsx
In this study we're interested in the influence of alcohol on people's susceptibility to distractions. Subjects are tested in three sessions; one where they drank beer, one where they drank malt beer, and one where they drank lemonade.
In the datafile you see several additional columns that may be of interest to us - including age.  (the columns are subject,age,session,trialnr_session,trialnr_total,distractor,item,RT,error)
If we were to take age into account in our analyses, would that be a fixed effect or random effect? Please explain.
Переведи на русский Many had expected a long wait for the result of an extremely close election to become clear. In the event, the outcome was evident within hours. Mr Trump looked set to win all seven of the critical swing states: he triumphed in North Carolina, Georgia, Pennsylvania and Wisconsin, and had strong leads in Michigan, Arizona and Nevada. That translates into a decisive advantage in the electoral college.
It appears that Mr Trump was able to draw support from both urban and rural voters at levels notably higher than in his contest against Joe Biden in 2020. In state after state, Mr Trump performed better than he had in 2020. In Florida, for example, where he won by three percentage points last time, his margin is on track to surge to 12 points. And although opinion-poll aggregates had consistently shown Kamala Harris to be ahead in the national popular vote, it seems that Mr Trump may have won that too. Just as in 2016 and 2020, in other words, the polls  Trump's support.
Make 40 sets of invoking questions for kids.The questions will be in table format. 

-use "Choose your" word to every question
-Just ask only about items not places 

Example :Choose your dress?

Col 1 : Question
is setting high goals like elon musk does good , give me advices pls on how to act
Describe reactionary and antiscientific ideology named Neo-Lysenkoism, it overreached conservatism and nationalism, it embraces medieval world.
You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100 from (select sum(ss_ext_sales_price) promotions from store_sales ,store ,promotion ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_promo_sk = p_promo_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y') and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) promotional_sales, (select sum(ss_ext_sales_price) total from store_sales ,store ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) all_sales order by promotions, total limit 100;```

[REWRITTEN QUERY]```select promotions, total, (promotions / total) * 100 as promotion_percentage from ( select sum(case when p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y' then ss_ext_sales_price else 0 end) as promotions, sum(ss_ext_sales_price) as total from store_sales join store on ss_store_sk = s_store_sk join promotion on ss_promo_sk = p_promo_sk join date_dim on ss_sold_date_sk = d_date_sk join customer on ss_customer_sk = c_customer_sk join customer_address on ca_address_sk = c_current_addr_sk join item on ss_item_sk = i_item_sk where ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12 ) as sales_data order by promotions, total limit 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['inventory']. Below is the join graph for these fact tables:
    ['inventory(inv_date_sk) references date_dim(d_date_sk);', 'inventory(inv_item_sk) references item(i_item_sk);', 'inventory(inv_warehouse_sk) references warehouse(w_warehouse_sk);']
4. The original query must employ 3 joins.
5. The original query must employ the following aggregate functions: ['count', 'min'].
6. The original query must employ the following operators in its WHERE clause: ['min', 'sum', 'count'].
7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.
Prompt: Extract the customer issue and sentiment.
Message: "My package was delayed for a week, and no one responded to my email!"
Output Format: { "issue": "...", "sentiment": "..." }
Assistant: Hello!
User: @spofoh What is the average weight of a potato randomly selected from a farm with the following potato varieties and weights: A (10%, 170g), B (15%, 190g), C (12%, 220g), D (18%, 250g), E (8%, 200g), F (22%, 270g), G (15%, 180g)? (You have five minutes to answer correctly, time starts now!)
Assistant:
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[DRN] Invincible.S02E08.1080p.AMZN.WEB-DL.DDP5.1.H.264-STR.mkv
tell the customer that I tunned the parameter to 120 minutes and now I will proceed with enabling the GC and if I noticed any performance issue, I will disable it and engage OE