agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Sep 28 13:17:02 vmi2169423 postfix/pickup[212924]: 44707D60A2B: uid=0 from=<root>
Sep 28 13:17:02 vmi2169423 postfix/cleanup[210640]: 44707D60A2B: message-id=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>
Sep 28 13:17:02 vmi2169423 postfix/qmgr[139066]: 44707D60A2B: from=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>, size=613, nrcpt=1 (queue active)
Sep 28 13:17:02 vmi2169423 postfix/pickup[212924]: 4E00FD60A2C: uid=0 from=<root>
Sep 28 13:17:02 vmi2169423 postfix/cleanup[204747]: 4E00FD60A2C: message-id=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>
Sep 28 13:17:02 vmi2169423 postfix/pipe[204745]: 44707D60A2B: to=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>, orig_to=<root>, relay=dovecot, delay=0.06, delays=0.02/0/0/0.03, dsn=5.1.1, status=bounced (user unknown)
Sep 28 13:17:02 vmi2169423 postfix/qmgr[139066]: 4E00FD60A2C: from=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>, size=662, nrcpt=1 (queue active)
Sep 28 13:17:02 vmi2169423 postfix/cleanup[210640]: 50758D60A2E: message-id=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>
Sep 28 13:17:02 vmi2169423 postfix/bounce[212750]: 44707D60A2B: sender non-delivery notification: 50758D60A2E
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Sep 28 13:17:02 vmi2169423 postfix/pipe[212566]: 4E00FD60A2C: to=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>, orig_to=<root>, relay=dovecot, delay=0.03, delays=0.01/0/0/0.02, dsn=5.1.1, status=bounced (user unknown)
Sep 28 13:17:02 vmi2169423 postfix/pipe[212567]: 50758D60A2E: to=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>, relay=dovecot, delay=0.03, delays=0.01/0/0/0.02, dsn=5.1.1, status=bounced (user unknown)
Sep 28 13:17:02 vmi2169423 postfix/cleanup[204747]: 5612BD60A2B: message-id=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>
Sep 28 13:17:02 vmi2169423 postfix/qmgr[139066]: 50758D60A2E: removed
Sep 28 13:17:02 vmi2169423 postfix/bounce[212750]: 4E00FD60A2C: sender non-delivery notification: 5612BD60A2B
Sep 28 13:17:02 vmi2169423 postfix/qmgr[139066]: 4E00FD60A2C: removed
Sep 28 13:17:02 vmi2169423 postfix/qmgr[139066]: 5612BD60A2B: from=<>, size=2441, nrcpt=1 (queue active)
Sep 28 13:17:02 vmi2169423 postfix/pipe[210644]: 5612BD60A2B: to=<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>, relay=dovecot, delay=0.02, delays=0.01/0/0/0.01, dsn=5.1.1, status=bounced (user unknown)
Sep 28 13:17:02 vmi2169423 postfix/qmgr[139066]: 5612BD60A2B: removed
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

نبداوا إليسار
talk about dental plaque and dental caries in detail
(In the school literature clubroom…)

Monika: “There, everything’s all set for today’s meeting! All that’s missing is MC.”

Sayori: “Oh, I wonder where he could be. He’s usually the first one here.”

Natsuki: “Knowing him, he probably got caught up playing video games again.”

Yuri: “Or maybe he lost track of time reading a book. Either way, I’m sure he’ll be here soon.”

Monika: “Yeah, let’s give him a few more minutes before we start. I’m sure he wouldn’t want to miss out on discussing our latest book club pick.”

Sayori: (rubbing her belly) “Ow!”

Monika: “Sayori, are you okay? What happened?”

Sayori: “Oh, it’s just the little one starting to kick again. I think she’s excited for our meeting too!”

Natsuki: “Aww, that’s so cute! I can’t believe how close you are to your due date already.”

Yuri: “I’m sure your baby will grow up to love literature just like us. It’s never too early to start them on good books.”

Sayori: “I hope so! I can’t wait to share all my favorite stories with her. And who knows, maybe she’ll even join the literature club one day.”

Monika: “That would be wonderful! Literature has a way of bringing people together and creating lasting bonds.”

(Just then, MC walks in, looking a little flustered.)

MC: “Sorry I’m late, everyone. I got lost in a good book on the way here.”

Monika: “No worries, MC. We were just talking about how literature can bring people together.”

Sayori: “Oh, and the baby’s kicking in excitement too! Look!”

MC: “That’s so cool! I can’t wait to meet her when she’s born. And I’m excited to discuss our latest book club pick with all of you.”

Natsuki: “Well, now that everyone’s here, let’s get started! Who wants to kick things off?”

Sayori: “Was that a pun, Natsuki? Because I think our little bookworm here would approve of that!”
Rewrite the Whole 45 episodes of Pokemon BW: Adventures in Unova  by writing out episode summaries using the following structure below: Set 5 (BW Episode 098-BW Episode 124) :this set of episodes would be mostly dedicated to the N and Team Plasma storyline based on the first Pokemon Black and White games : episodes about a public Pokemon Liberation movement by BW1 Team Plamsa, along with Episodes with N befriending Ash (while secretly being the main antagonist) only to later reveal his true allegiances, as well as episodes that go have deep studies into the mythology of Unova’s Legendary dragons (Iris and the Village of Dragons would really come into play here), along with episodes with Team Rocket fighting against Team Plasma for the conquest of the Unova Region
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We invest significant resources in information technology system enhancements in order to provide functionality and security at an appropriate level. We may not be able to successfully implement and integrate future system enhancements, which could adversely impact the ability to provide timely and accurate financial information in compliance with legal and regulatory requirements, which could result in sanctions from regulatory authorities. Such sanctions could include fines and suspension of trading in our stock, among others. In addition, future system enhancements could have higher than expected costs and/or result in operating inefficiencies, which could increase the costs associated with the implementation as well as ongoing operations.
Assistant:
write a report about  Gitex 2023 event in Dubai
Sash Lilac & Naruto Uzumaki got into the world Sid Meier’s Civilization. year/Move 1-4 (Lilac x Naruto)
Assistant: 
User: Respond in English or Simplified Chinese: you
Assistant:
Describe the significance, strengths, weaknesses, and hurdles of the Indian name 'Shivakumar', strictly keep it under 350 characters. add emojis if you can to make it more attractive.
How much i have to pay for membership fee? Правильно ли это
Generate a description with keywords and SEOs for Sunlit Beauty: Elegant Woman in White Blouse and Dress with Radiant Smile - Bedroom Bliss. Include social media platforms on which this stock photographic can be applied to for social media at the end, do not include the flags starting with '--' like (--ar --stylize) and its value, Don't mention on resolutions. Don't include HTML or '<br/>'  or '<strong>' for formatting only newline '
'
Сделай мне ссылку на заявление.
<button type="button" class="btn btn-link" data-toggle="modal" data-target="#w5">Согласие на обработку персональных данных (распечатать, заполнить и приложить скан)</button> <div id="w5" class="image-in-popup-viewer modal" role="dialog" tabindex="-1" aria-hidden="true" aria-labelledby="w5-label"> <div class="modal-dialog modal-lg" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 id="w5-label" class="modal-title">Шаблон «Согласие на обработку персональных данных (распечатать, заполнить и приложить скан)»</h5> <button type="button" class="close close-popup-image-view"><span aria-hidden="true">&times;</span></button> </div> <div class="d-flex justify-content-center modal-body"> <iframe src="/site/download-attachment-type-template?id=2&_referrer=%2Fbachelor%2Fapplication%3Fid%3D661" width="100%" height="500hv"></iframe> </div> </div> </div> </div> 

текущая страница https://priem.mipt.ru/bachelor/application?id=661&_referrer=%2Fbachelor%2Fege%3Fid%3D661
Make a comedic and vividly detailed story set in a universe where Germany won WWII about a sculptor making a marble statue of Tom Holland due to his Anglo-Saxon ethnicity. Tom enters the studio and undresses, posing for the statue in the buff. Describe how the statue looks during the process. Describe Tom’s reaction to the statue. Add dialogue between the sculptor and Tom throughout
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
Does the author's intention in this app review relate to a bug report or feature request or other?
review: Disable all internet access from within VLC'
intention: '
Assistant:
So-hee records the work shown.

Line 1:	
Line 2:	
Line 3:	
Line 4:	
Line 5:	          blank
Part A

What value makes the equation in line 5 a true number sentence?

Part B

What does your answer to part A indicate about line 4?

Responses

The product of 
 and 
 is 
.
The product of 3 and 2 is 6.

The product of 
 and 
 is 
.
The product of negative 3 and 2 is negative 6.

The product of 
 and 
 is 
.
The product of negative 3 and negative 2 is 6.

The product of 
 and 
 is 
.
The product of negative 3 and negative 2 is negative 6.
Skip to navigation
Make a 10 Female!SCP Harem for Joshua Robertson, and Joshua is a passionate and enthusiastic comic book nerd. He is deeply knowledgeable about comic book characters, especially superheroes, and is always eager to discuss and analyze their stories and powers. He is enthusiastic, imaginative, creative, dedicated, friendly, and observant. Joshua possesses a friendly and animated voice, reflecting his passion for comics. He has an oval-shaped face with expressive features, including large round eyes, warm brown eyes, fair skin, straight nose, medium-sized lips, medium-length eyelashes, and well-defined eyebrows. His hair is soft and wavy, medium-length, dark brown with caramel highlights. Joshua’s powers include teleportation, enhanced agility and reflexes, cosplay skills, research and analysis, and dark side mastery. He has a high level of intelligence (IQ of 140) and can fully control his dark side.
Assistant: Hello! How can i help you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه فقط دون تزويدي بأي معلومات أو تفاصيل زائدة مع النص النهائي.
Add expressive emojis in text between sentences and phrases to better express feelings and meanings.
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
فويس اوفر becomes Voiceover
اوبو becomes Oppo
فوكالايزر becomes Vocalizer
انفيجن becomes Envision
بي ماي ايس becomes Be My Eyes
ميت becomes Meet
ام اكس بلاير برو becomes MX Player Pro
سمارت اوديو بوك بلير becomes Smart AudioBook Player
تك فريدم becomes Tech-Freedom
فويسس becomes voices
فويس اللاود ريدر becomes Voice Allowed Reader
تكنو becomes tecno
اي اي becomes AI
جوجل becomes Google
and so on.
Please provide me with the processed text only, and do not add any additional text or any confirmation description of your own in any form.
This is the text:
تم سماع محاضرات النبر وحضورها مباشر معظمها يعني
Assistant:
Would it be realistic, if, in an alien invasion film, or series, certain Governments of the world tried to make deals with the Aliens, even alliances?
What%20are%20some%20names%20of%20a%20minecraft%20mod%20that%20punishes%20players%20for%20speaking%20forbidden%20words%3F%20
which animal controller allows me to quickly animate and interact with this 3D model: https://www.cgtrader.com/3d-models/animals/dinosaur/dragon-d91e97ed-01ce-4b04-a793-767b480b2238
The following is from the Hibernate 6.3 documentation.:
3.12. Converters
A JPA AttributeConverter is responsible for:

converting a given Java type to one of the types listed above, and/or

perform any other sort of pre- and post-processing you might need to perform on a basic attribute value before writing and reading it to or from the database.

Converters substantially widen the set of attribute types that can be handled by JPA.

There are two ways to apply a converter:

the @Convert annotation applies an AttributeConverter to a particular entity attribute, or

the @Converter annotation (or, alternatively, the @ConverterRegistration annotation) registers an AttributeConverter for automatic application to all attributes of a given type.

For example, the following converter will be automatically applied to any attribute of type BitSet, and takes care of persisting the BitSet to a column of type varbinary:

@Converter(autoApply = true)
public static class EnumSetConverter implements AttributeConverter<EnumSet<DayOfWeek>,Integer> {
    @Override
    public Integer convertToDatabaseColumn(EnumSet<DayOfWeek> enumSet) {
        int encoded = 0;
        var values = DayOfWeek.values();
        for (int i = 0; i<values.length; i++) {
            if (enumSet.contains(values[i])) {
                encoded |= 1<<i;
            }
        }
        return encoded;
    }

    @Override
    public EnumSet<DayOfWeek> convertToEntityAttribute(Integer encoded) {
        var set = EnumSet.noneOf(DayOfWeek.class);
        var values = DayOfWeek.values();
        for (int i = 0; i<values.length; i++) {
            if (((1<<i) & encoded) != 0) {
                set.add(values[i]);
            }
        }
        return set;
    }
}
On the other hand, if we don’t set autoapply=true, then we must explicitly apply the converter using the @Convert annotation:

@Convert(converter = BitSetConverter.class)
@Basic(optional = false)
BitSet bitset;
All this is nice, but it probably won’t surprise you that Hibernate goes beyond what is required by JPA.

Is the above accidentally using BitSet and EnumSet interchangeably when it shouldn't, or am I missing something? Be concise.
The article "Gas Cooking: Kitchen Ventilation, and Exposure to Combustion Products" (Indoor Air, 2006: 65-73) reported that for a sample of 60 kitchens with gas cooking appliances monitored during a one-week period, the sample mean CO2 level (ppm) was 661.96, and the sample standard deviation was 164.27.

a.  Calculate and interpret a 95% (two-sided) confidence interval for the true average CO2 level in the population of all homes from which the sample was selected.

Confidence Interval:  (round your answers to 2 decimal places)

(
   , 
   )

Interpret your confidence interval.  You will not receive a score for this until the instructor manually grades it.

  

b.  Suppose the investigators had made a rough guess of 176 for the value of 
 before collecting data.  What sample size would be necessary to obtain an interval width of 50 ppm for a confidence level of 95%?
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[nunadrama]What If 9.480p.mp4
give me summary :"agreement with previous work, two main processes can be identified on anodic
oxidation: (a) oxidation of Cu to Cu20; (b) formation of Cu(II). These processes
are represented by peaks (i) and the doublet (it) and (iii), respectively, i n Fig. 1.
In addition, a small peak near the oxygen evolution region has been observed
in 1 M KOH which has previously been attributed to the formation of Cu(III) lz.
The peak due to the formation of CuzO is very small in comparison to the
doublet and occurs at about -400 mV (with respect to the Hg/HgO, 1 M KOH
electrode). A careful examination at high sensitivity and especially at higher sweep
rates indicates the presence of a smaller peak at even-lower potentials (approx. - 550
mV).
Ring-disk experiments by Miller 12 have detected soluble Cu(I) in this region of
the voltammogram and by analogy with the oxidation of Ag to Agz O in KOH 16
the soluble species is probably Cu(OH)2. Hence the reaction at the pre-peak
can be represented by
Cu + 2 OH- ~ Cu(OH)2 + e (a)
The formation of Cu20 has been attributed 8'1° to
2 Cu+2 OH- ~ Cu20+H20+2e (b)
for which E ° vs. Hg/HgO electrode is -0.45 V s. The potential of peak (i) Fig. 1,
is consistent with this value and therefore this peak is due to reaction (b) occurring
with production of surface CuzO.
The two components of the doublet occur at about -100 mV and 0 mV,
and can be correlated with the formation of Cu(II) by a consideration of the
E ° values for CU20/CuO , -250 mV 8, and Cu20/Cu(OH)2 , -170 mV 8, and
earlier voltammetric work on copper 10. The profile changes considerably with sweep
rate: at slow speeds, peak (iii) predominates while at high speeds, peak (it) is
dominant. Because of the small separation of these peaks and because of the possible
interference with each other of the processes which are occurring in this potential
region it is not possible to apply the criterion of peak height and peak potential
dependence upon sweep rate with certainty. However, some conclusions can be drawn
under conditions where one or other of the peaks is predominant.
For peak (it) this would apply to high sweep rates (i.e. above 100 mV s-~).
It was found under these conditions that ipOC/)~. For a reaction such as:
Cu + 4 OH- ~ Cu(OH) 2- + 2e (c)
taking place on a clean copper surface without the formation of a surface film,
peak currents should only be observed if diffusion of OH- ions is the limiting process.
According to Delahay ~7,
ip = 3.67 x lOSn~Ac°D~v ½ (1)
where n=No. of electrons in the reaction, A=electrode surface area, c°=bulk
concentration of OH- in mol cm-3, D = diffusion coefficient of OH- ion, v = sweep
rate in V s-1.
The slope in 1 M KOH gives a value for the diffusion coefficient of OH- ion
of about 10 -9 cm 2 s-1. This is much lower than the expected value of about
10 -5 cnl 2 s -I."
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We may be adversely affected by patients’ unwillingness to pay for treatment by our clinicians. Higher numbers of unemployed individuals generally translate into more individuals without healthcare insurance to help pay for services, thereby increasing the potential for persons to elect not to seek treatment if they cannot afford to self-pay. Growth of patient receivables or deterioration in the ability to collect on these accounts, due to changes in economic conditions or otherwise, could have an adverse effect on our business, results of operations and financial condition. In addition, patients with high deductible insurance plans may be less likely to seek treatment as a result of higher expected out-of-pocket costs.
Assistant:
Hi. Can you speak Russian?
Write a 100% more realistic, more detailed and more accurate plain text of the full back cover for the 2001 VHS of The Tale of the Bunny Picnic (including technical specifications, legal disclaimers, copyright and trademark notices, distribution, etc).
bitconnect ponzi scheme
wifi pass