Create Abstract Automatically From Research Paper Details

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Create Abstract

Choose the appropriate design of the abstract:

Abstract style:

APA Style

Input language:

English

Title:

0/50 Symbols.

Author:

Authors:

Formulation of the problem:

«Statement of the problem (that a research project or article is aimed)»

Method of research:

«Ways to solve the task (specific steps addressing the existing task). If you write an abstract for a literature review, enter data on the “research selection criteria” in this field»

Results:

«Quantitative or qualitative results of your research»

Conclusions:

«The scope of implementation of the research results, to what extent the work carried out has broadened existing ideas about the issue under study or proposed a new solution to the existing problem»

Keywords: (service for keyword list creation)

«Specify a short list of keywords, in order to more effective search of your document by researchers and databases»

https://www.4author.com/annotation/

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Free Alternative To SPSS - GNU PSPP

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GNU PSPP is a program for statistical analysis of sampled data. It is a free as in freedom replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions.

The most important of these exceptions are, that there are no “time bombs”; your copy of PSPP will not “expire” or deliberately stop working in the future. Neither are there any artificial limits on the number of cases or variables which you can use. There are no additional packages to purchase in order to get “advanced” functions; all functionality that PSPP currently supports is in the core package.

PSPP is a stable and reliable application. It can perform descriptive statistics, T-tests, anova, linear and logistic regression, measures of association, cluster analysis, reliability and factor analysis, non-parametric tests and more. Its backend is designed to perform its analyses as fast as possible, regardless of the size of the input data. You can use PSPP with its graphical interface or the more traditional syntax commands.

A brief list of some of the PSPP's features follows below. We also made available a page with screenshots and sample output. PSPP has:

• Support for over 1 billion cases.
• Support for over 1 billion variables.
• Syntax and data files which are compatible with those of SPSS.
• A choice of terminal or graphical user interface.
• A choice of text, postscript, pdf, opendocument or html output formats.
• Inter-operability with Gnumeric, LibreOffice, OpenOffice.Org and other free software.
• Easy data import from spreadsheets, text files and database sources.
• The capability to open, analyse and edit two or more datasets concurrently. They can also be merged, joined or concatenated.
• A user interface supporting all common character sets and which has been translated to multiple languages.
• Fast statistical procedures, even on very large data sets.
• No license fees.
• No expiration period.
• No unethical “end user license agreements”.
• A fully indexed user manual.
• Freedom ensured; It is licensed under the GPLv3 or later.
• Portability; Runs on many different computers and many different operating systems (GNU or GNU/Linux are the prefered platforms, but we have had many reports that it runs well on other systems too).

PSPP is particularly aimed at statisticians, social scientists and students requiring fast convenient analysis of sampled data.

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REFERENCE:

https://www.gnu.org/software/pspp/

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DOWNLOAD: 

https://www.gnu.org/software/pspp/get.html

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ZoteroBib - helps you build a bibliography instantly from any computer or device, without creating an account or installing any software

 

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Cite anything

ZoteroBib helps you build a bibliography instantly from any computer or device, without creating an account or installing any software. It’s brought to you by the team behind Zotero, the powerful open-source research tool recommended by thousands of universities worldwide, so you can trust it to help you seamlessly add sources and produce perfect bibliographies. If you need to reuse sources across multiple projects or build a shared research library, we recommend using Zotero instead.

Adding a bibliography entry

Simply find what you’re looking for in another browser tab and copy the page URL to the ZoteroBib search bar. ZoteroBib can automatically pull in data from newspaper and magazine articles, library catalogs, journal articles, sites like Amazon and Google Books, and much more. You can also paste or type in an ISBN, DOI, PMID, or arXiv ID, or you can search by title.

Manual entry

If automatic import doesn’t find what you’re looking for or you’re entering something without a URL or identifier, you can enter the reference information by hand.

Bibliography title

To rename your bibliography, just click its title. A title can be useful if you’re switching between multiple projects or sharing a bibliography with others.

Editing an item

You might need to add or change a few fields after adding an item. Click on a bibliography entry to make manual changes.

Deleting items

Click the Remove icon next to a bibliography entry to delete it. To start a new bibliography, click Delete All to remove all entries.

Style selection

Format your bibliography using APA, MLA, Chicago / Turabian, Harvard, or any of the 10,000+ other CSL styles.

Copy Citation / Note

As you’re writing, you can quickly generate parenthetical citations or footnotes /endnotes to paste into your document without typing names or dates by hand.

Export

When you’re done, you can copy a formatted bibliography to the clipboard and paste it into your document. You can also export HTML to add to a webpage, an RTF document to open in a word processor, or a RIS or BibTeX file to import into a reference manager.

Autosave

ZoteroBib automatically saves your bibliography to your browser’s local storage — you can close the page and return to it anytime. (If you’re using private / incognito mode in your browser, your bibliography will be cleared when you close the window.)

Link to this version

If you want to edit your bibliography on another device, share it with someone else, or switch to another bibliography, you can generate a link to a copy of the current version on zbib.org. Use the link to retrieve your bibliography later.

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https://zbib.org/

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Getting Started With Machine Learning On AWS Sagemaker

 

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Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.

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SageMaker enables developers to operate at a number of levels of abstraction when training and deploying machine learning models. At its highest level of abstraction, SageMaker provides pre-trained ML models that can be deployed as-is.

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In addition, SageMaker provides a number of built-in ML algorithms that developers can train on their own data.Further, SageMaker provides managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch.

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Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, AWS Batch for offline batch processing, or Amazon Kinesis for real-time processing.

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REFERENCE:

https://en.wikipedia.org/wiki/Amazon_SageMaker

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TUTORIAL:

In this tutorial, you will assume the role of a machine learning developer working at a bank. You have been asked to develop a machine learning model to predict whether a customer will enroll for a certificate of deposit (CD).


In this tutorial, you learn how to:

• Create a SageMaker notebook instance
• Prepare the data
• Train the model to learn from the data
• Deploy the model
• Evaluate your ML model's performance

https://aws.amazon.com/getting-started/hands-on/build-train-deploy-machine-learning-model-sagemaker/

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An empirical study of emoji usage on Twitter in linguistic and national contexts

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Abstract:

Emojis or ‘picture characters’ have become ubiquitous in modern-day digital communication, including social media sharing and smartphone texting. 

Despite this ubiquity, many questions remain about their usage, especially with respect to global variations in language and country. 

These questions are important, in part because they reveal how people communicate digitally on social platforms, but also because they provide a lens through which different regions and cultures can be studied. 

In this paper, we conduct a principled, quantitative study to understand emoji usage in terms of linguistic and country correlates. 

Our study involves 30 languages and countries each, and is conducted over tens of millions of tweets collected from the Twitter decahose over an entire month. 

Drawing on both statistical measures and information theory, our results reveal that, not only does emoji usage have strong dependencies at both the language and country level, but that some languages and countries are much more constrained in the diversity of their emoji usage. 

However, we also discover that the ‘popularity’ of emojis, both globally and within the context of a given language, follows a robust and invariant trend that emerges fairly quickly (over just a day’s worth of data) and cannot be explained either by a power-law or Heap’s law-like distribution.

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https://www.sciencedirect.com/science/article/pii/S2468696421000318

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https://www.semanticscholar.org/paper/An-empirical-study-of-emoji-usage-on-Twitter-in-and-Kejriwal-Wang/506411ea9600fb78f54aa9dcb2be537af56980b4

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https://doi.org/10.1016/j.osnem.2021.100149

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Emojis influence emotional communication, social attributions, and information processing

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Abstract:

Many emojis symbolize nonverbal cues that are used during face-to-face communication. 

Despite their popularity, few studies have examined how emojis influence digital interactions. 

The present study addresses this gap by measuring the impact of emojis on emotion interpretation, social attributions, and information processing. 

Participants read messages that are typical of social exchanges in instant text messaging (IM) accompanied by emojis that mimic negative, positive and neutral facial expressions. 

Sentence valence and emoji valence were paired in a fully crossed design such that verbal and nonverbal messages were either congruent or incongruent. 

Perceived emotional state of the sender, perceived warmth, and patterns of eye movements that reflect information processing were measured. 

A negativity effect was observed whereby the sender's mood was perceived as negative when a negative emoji and/or a negative sentence were presented. 

Moreover, the presence of a negative emoji intensified the perceived negativity of negative sentences. 

Adding a positive emoji to a message increased the perceived warmth of the sender. 

Finally, processing speed and understanding of verbal messages was enhanced by the presence of congruent emojis. 

Our results therefore support the use of emojis, and in particular positive emojis, to improve communication, express feelings, and make a positive impression during socially-driven digital interactions.

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https://www.sciencedirect.com/science/article/abs/pii/S0747563221000443

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https://www.semanticscholar.org/paper/Emojis-influence-emotional-communication%2C-social-Boutet-LeBlanc/c7d4859af5e1f27a6605697b9c17e704d7926704

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https://doi.org/10.1016/j.chb.2021.106722

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How emotional are emoji?: Exploring the effect of emotional valence on the processing of emoji stimuli

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Abstract:

Emoji are vastly becoming an integral part of everyday communication, yet little is understood about the extent to which these are processed emotionally. 

Previous research shows that there is a processing advantage for emotionally-valenced words over neutral ones, therefore if emoji are indeed emotional, one could expect an equivalent processing advantage. 

In the Pilot Study, participants (N = 44) completed a lexical decision task to explore accuracy and response latency of word, face and emoji stimuli. 

This stimuli varied in emotional valence (positive vs. neutral). 

Main effects were found for stimuli type and valence on both accuracy and latency, although the interaction for accuracy was not significant. 

That is, there were processing advantages of positively-valenced stimuli over neutral ones, across all stimuli types. 

Also, faces and emoji were processed significantly more quickly than words, and latencies between face and emoji stimuli, irrespective of valence were largely equivalent. 

The Main Study recruited 33 participants to undertake a modified and extended version of the lexical decision task, which included three valence conditions (positive, negative and neutral) per stimuli type. 

Although no main effects were found for accuracy, there was a significant main effect found for stimuli but not for valence on latency. 

Namely, that word stimuli irrespective of valence were processed significantly more slowly than face or emoji stimuli. 

There was not a significant interaction between stimuli and valence, however. 

Therefore, overall although there was partial support for a processing advantage of emoji stimuli, this was not replicated across the studies reported here, suggesting additional work may be needed to corroborate further evidence.

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https://www.sciencedirect.com/science/article/abs/pii/S0747563220303952

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https://www.semanticscholar.org/paper/How-emotional-are-emoji%3A-Exploring-the-effect-of-on-Kaye-Rodriguez-Cuadrado/adb03320ca10aecb0f8392dd4a695baade573897

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https://doi.org/10.1016/j.chb.2020.106648

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Dataset:

https://osf.io/asmh7/?view_only=14c499529edf4acfb28b3c327af234ab

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PlumX Metrics:

https://plu.mx/plum/a/?doi=10.1016/j.chb.2020.106648

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Seq2Emoji: A hybrid sequence generation model for short text emoji prediction

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Abstract: 

As a new form of visual language, emojis are widely used in social media for their vivid image and rich meaning. 

Predicting the most likely emojis that fit a particular short text has become an important and challenging task in both academia and industry. 

In this paper, we propose a hybrid sequence generation model, Seq2Emoji, to predict multiple emojis based on a short text. 

Seq2Emoji is an encoder–decoder model, in which we consider the correlations between emojis and take the emoji prediction task as a sequence generation problem. 

It extracts features through a hierarchical structure and self-attention mechanism and decodes them with a composite recurrent neural network before predicting emojis. 

During the prediction, Diverse Beam Search algorithm is also introduced to increase the diversity of predicted emojis. 

Experiments are carried out on our collected Weibo dataset (Chinese) and the results show that our proposed Seq2Emoji model is superior to the competitive models in both accuracy and diversity of emoji prediction.

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https://www.sciencedirect.com/science/article/abs/pii/S095070512030856X

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https://www.semanticscholar.org/paper/Seq2Emoji%3A-A-hybrid-sequence-generation-model-for-Peng-Zhao/ab802b7223e33a38ceb8492c34ca0aaf45c47182

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https://doi.org/10.1016/j.knosys.2020.106727

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