Prompt: Transformer-based model designed specifically for Arabic language understanding

Output: Transformer-based model designed specifically for Arabic language understanding

Output:
Title: AraBERT: Transformer-based Model for Arabic Language Understanding

Abstract: The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks. The pretrained araBERT models are publicly available on https://github.com/aub-mind/arabert hoping to encourage research and applications for Arabic NLP.


Prompt: do switches use ip or mac?

Output: do switches use ip or mac?

Output:
Superuser Stackexchange

Q: IP Address vs MAC addresses why do we need ip addresses if mac address is enough to identify the machine???

A: There are many reasons. For example: MAC address is bound to hardware. It means you can't or rather are not supposed to have the same MAC on different hardware. Imagine that google has to change DNS binding of "google.com" to "MAC" address each time they change some network card on front-end server.
Another reason is that MAC addresses are assigned by producers while IP addresses are assigned by network admins. This allows building hierarchy of addresses that is root of packets routing. This is because way from IP A1.A2.A3.A4 to B1.B2.B3.B4 is easily predictable and way from MAC#1 to MAC#2 isn't.
Next reason is that MAC is Ethernet-related stuff and there are other network technologies that might not use MAC at all.


Prompt: do switches use ip or mac?

Output: do switches use ip or mac?

Output:
Serverfault Stackexchange

Q: Deny Static IP Assignments I am currently using a SonicWall Pro 2040 with DHCP enabled, but only with static IPs mapped to specific MAC addresses. There is no dynamic IP scope being used. Currently, no DHCP request will resolve an IP, as desired.
However, if a server or workstation network interface is still configured with a user-defined, static IP address, it works fine. I would like potential users who assign their machines with static IPs to not work.
Is there a way to deny these types of connections whose MAC address is not approved?

A: There is no way to configure a DHCP server to deny static IP addresses. If you think about it, there is a direct path between hosts on the network that simply does not go through the SonicWall:
            SonicWall
            LAN Port
                ^
                |
                v
HostA <----> Switch <----> HostB

So if you want to filter on MAC address to stop HostA from advertising an IP address on the network, you need to do so at the switch. As an example, if your switch is a Cisco, the command to use is switchport port-security.


Prompt: can I do multithreading with fastapi

Output: can I do multithreading with fastapi

Output:
FastAPI

FastAPI is a modern web framework first released in 2018 for building HTTP-based service APIs in Python. It is used for building APIs with Python 3.8+ based on standard Python-type hints.
FastAPI is based on Pydantic and uses type hints to validate, serialize and deserialize data. It also automatically generates OpenAPI documentation for APIs built with it.
Components
Pydantic
Pydantic is a data validation library for Python. While writing code in an IDE, Pydantic provides type hints for schema validation and serialization through type annotations.
Starlette
Starlette is a lightweight ASGI framework/toolkit, to support async functionality in Python.
Uvicorn
Uvicorn is a minimal low-level server/application web server for async frameworks, following the ASGI specification. Technically, it implements a multi-process model with one main process, which is responsible for managing a pool of worker processes and distributing incoming HTTP requests to them. The number of worker processes is pre-configured, but can also be adjusted up or down at runtime.
Example
The following code shows a simple web application that displays "Hello World!" when visited:
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():


Prompt: can I do multithreading with fastapi

Output: can I do multithreading with fastapi

Output:
FastAPI

FastAPI is a modern web framework first released in 2018 for building HTTP-based service APIs in Python. It is used for building APIs with Python 3.8+ based on standard Python-type hints.
FastAPI is based on Pydantic and uses type hints to validate, serialize and deserialize data. It also automatically generates OpenAPI documentation for APIs built with it.
Components
Pydantic
Pydantic is a data validation library for Python. While writing code in an IDE, Pydantic provides type hints for schema validation and serialization through type annotations.
Starlette
Starlette is a lightweight ASGI framework/toolkit, to support async functionality in Python.
Uvicorn
Uvicorn is a minimal low-level server/application web server for async frameworks, following the ASGI specification. Technically, it implements a multi-process model with one main process, which is responsible for managing a pool of worker processes and distributing incoming HTTP requests to them. The number of worker processes is pre-configured, but can also be adjusted up or down at runtime.
Example
The following code shows a simple web application that displays "Hello World!" when visited:
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():


Prompt: How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?

Output: How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?

Output:
Evaluation measures (information retrieval)

Some authors choose to interpolate the  function to reduce the impact of "wiggles" in the curve. For example, the PASCAL Visual Object Classes challenge (a benchmark for computer vision object detection) until 2010 computed the average precision by averaging the precision over a set of evenly spaced recall levels {0, 0.1, 0.2, ... 1.0}:
where  is an interpolated precision that takes the maximum precision over all recalls greater than :
.
An alternative is to derive an analytical  function by assuming a particular parametric distribution for the underlying decision values. For example, a binormal precision-recall curve can be obtained by assuming decision values in both classes to follow a Gaussian distribution.
The minimum achievable AveP for a given classification task is given by:
Precision at k
For modern (web-scale) information retrieval, recall is no longer a meaningful metric, as many queries have thousands of relevant documents, and few users will be interested in reading all of them. Precision at k documents (P@k) is still a useful metric (e.g., P@10 or "Precision at 10" corresponds to the number of relevant results among the top 10 retrieved documents), but fails to take into account the positions of the relevant documents among the top k.  Another shortcoming is that on a query with fewer relevant results than k, even a perfect system will have a score less than 1.  It is easier to score manually since only the top k results need to be examined to determine if they are relevant or not.


Prompt: How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?

Output: How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?

Output:
Information retrieval

Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.  The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications.


Prompt: Impact of AI chatbots on academic integrity and plagiarism

Output: Impact of AI chatbots on academic integrity and plagiarism

Output:
Title: Will ChatGPT get you caught? Rethinking of Plagiarism Detection

Abstract: The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper.


Prompt: Impact of AI chatbots on academic integrity and plagiarism

Output: Impact of AI chatbots on academic integrity and plagiarism

Output:
Title: Will ChatGPT get you caught? Rethinking of Plagiarism Detection

Abstract: The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper.


Prompt: What are the main causes and effects of the Industrial Revolution?

Output: What are the main causes and effects of the Industrial Revolution?

Output:
Industrial Revolution

Primitivism Primitivism argues that the Industrial Revolution have created an un-natural frame of society and the world in which humans need to adapt to an un-natural urban landscape in which humans are perpetual cogs without personal autonomy.
Certain primitivists argue for a return to pre-industrial society, while others argue that technology such as modern medicine, and agriculture are all positive for humanity assuming they are controlled by and serve humanity and have no effect on the natural environment.
Pollution and ecological collapse
The Industrial Revolution has been criticised for leading to immense ecological and habitat destruction. It has led to immense decrease in the biodiversity of life on Earth. The Industrial revolution has been said to be inherently unsustainable and will lead to eventual collapse of society, mass hunger, starvation, and resource scarcity.
The Anthropocene
The Anthropocene is a proposed epoch or mass extinction coming from humanity (anthropo- is the Greek root for humanity). Since the start of the Industrial revolution humanity has permanently changed the Earth, such as immense decrease in biodiversity, and mass extinction caused by the Industrial revolution. The effects include permanent changes to the Earth's atmosphere and soil, forests, the mass destruction of the Industrial revolution has led to catastrophic impacts on the Earth. Most organisms are unable to adapt leading to mass extinction with the remaining undergoing evolutionary rescue, as a result of the Industrial revolution.


Prompt: What are the main causes and effects of the Industrial Revolution?

Output: What are the main causes and effects of the Industrial Revolution?

Output:
De-industrialisation of India

The fall in the hegemony of Mughals reduced the overall productivity of agriculture and reduced the supply of grains. The grain was the primary consumption good for the Indian workers and was non-tradeable. The reduction in the supply of grain resulted in the rise of its prices. This rise in prices and negative supply shock led to a rise in the nominal wages in the cotton and weaving industry. The increased competition from British cotton and rising nominal wages reduced the profitability of the cotton industry of India. Thus, the negative supply shock in agricultural production is also an important reason behind the de-industrialisation of cotton–industries.
The short run as well as long run impact on living standards and growth rate of GDP providing agriculture sector competitive advantage with strengthening of the productivity advance on the land at home or increasing openness to world in turn increases GDP in the short run. The causes of de-industrialisation are region or country specific as in the case of India in the 19th and 20th century. The colonial rule under the British led to the decline of textile and handicrafts industries through their policies and introduction of machine made goods in to the Indian market. Some of the causes of de-industrialisation in India during that period were:
Introduction of machine made goods in the Indian subcontinent at a cheaper rate, which led to the decline of the traditional textile industry of India.


Prompt: are electrical outlets on studs?

Output: are electrical outlets on studs?

Output:
Electronics Stackexchange

Q: Purpose of breadboard "studs" on base off micro breadboards? I recently bought some micro breadboards on Ebay.  Extremely useful little things.  However each have two awkward studs on their bases.  See the attached photo.   What I'm curious about is their purpose?  What might these "plugin" to or be used for?  I think I'm going to file them off.

A: They connect to a base that fixes them with regards to each other.


Prompt: are electrical outlets on studs?

Output: are electrical outlets on studs?

Output:
Math Stackexchange

Q: Write $\cos^2(x)$ as linear combination of $x \mapsto \sin(x)$ and $x \mapsto \cos(x)$
Can we write $\cos^2(x)$ as linear combination of $x \mapsto \sin(x)$ and  $x \mapsto \cos(x)$?

I know
$$
\cos^2(x)
= \frac{\cos(2x) + 1}{2}
= 1 - \sin^2(x)
= \cos(2x) + \sin^2(x)
$$
but none of these helped.
Then, I tried to solve
$$
\cos^2(x) = \alpha \sin(x) + \beta \cos(x)
$$
for the coefficients $\alpha, \beta \in \mathbb{R}$.
But when plugging in $x = 0$ I get $\beta = 1$ and for $x = \frac{\pi}{2}$ I get $\alpha = 0$. Plugging those values back in I obtain a false statement, and WolframAlpha can't do better!
This is from a numerical analysis exam and the second function is $x \mapsto \sqrt{2}\cos\left(\frac{\pi}{4} - x \right)$, which can easily be expressed in terms of $x \mapsto \sin(x)$ and $x \mapsto \cos(x)$ by the corresponding addition formula.

A: The function $f(x):=\cos^2 x$ has $f(x+\pi)\equiv f(x)$, but any linear combination $g$ of $\cos$ and $\sin$ has $g(x+\pi)\equiv -g(x)$.


Prompt: What are the differences between machine learning and deep learning?

Output: What are the differences between machine learning and deep learning?

Output:
Comparison of deep learning software

The following table compares notable software frameworks, libraries and computer programs for deep learning.
Deep-learning software by name
Comparison of compatibility of machine learning models


Prompt: What are the differences between machine learning and deep learning?

Output: What are the differences between machine learning and deep learning?

Output:
Deep learning

Overview Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.
Fundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a slightly more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face.
Importantly, a deep learning process can learn which features to optimally place in which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate upon. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction.


Prompt: when to use an air gap for dishwasher

Output: when to use an air gap for dishwasher

Output:
Air gap (networking)

An air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality.


Prompt: when to use an air gap for dishwasher

Output: when to use an air gap for dishwasher

Output:
Air gap (networking)

An air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality.