how to write a research paper computer science

Writing for Computer Science

• © 2014
• Latest edition
• Justin Zobel 0

University of Melbourne, Parkville, Australia

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• Extensive guidance on writing and presentation skills for researchers and practitioners in the field of Computer Science
• A comprehensive introduction to research methods and scientific writing for computer scientists
• An overview of the skills that a student needs to become an effective researcher
• Includes supplementary material: sn.pub/extras

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Table of contents (17 chapters)

Front matter, introduction.

Justin Zobel

Getting Started

Reading and reviewing, hypotheses, questions, and evidence, writing a paper, style specifics, punctuation, mathematics, graphs, figures, and tables, other professional writing, experimentation, statistical principles, presentations, back matter.

• Effective Communication
• Organization
• Presentation of Ideas
• Scientific Research
• Writing Style

About this book

All researchers need to write or speak about their work, and to have research  that is worth presenting. Based on the author's decades of experience as a researcher and advisor, this third edition provides detailed guidance on writing and presentations and a comprehensive introduction to research methods, the how-to of being a successful scientist. 

Topics include:

·         Development of ideas into research questions;

·         How to find, read, evaluate and referee other research;

·         Design and evaluation of experiments and appropriate use of statistics;

·         Ethics, the principles of science and examples of science gone wrong.

Much of the book is a step-by-step guide to effective communication, with advice on:

 ·         Writing style and editing;

·         Figures, graphs and tables;

·         Mathematics and algorithms;

·         Literature reviews and referees’ reports;

·         Structuring of arguments and results into papers and theses;

·         Writing of other professional documents;

·         Presentation of talks and posters.

Written in an accessible style and including handy checklists and exercises, Writing for Computer Science is not only an introduction to the doing and describing of research, but is a valuable reference for working scientists in the computing and mathematical sciences.

“This is a comprehensive guide on research methods and how to produce a scientific publication detailing one’s research in computer science … . a must-read for those doing research in CS and related fields. It will greatly benefit anyone who is involved in any kind of scientific research, as the examples are only from the CS field. Students, researchers, scientists, and other academicians involved in scientific research will improve both their research methods and writing by reading this book.” (Alexis Leon, Computing Reviews, July, 2015)

Authors and Affiliations

About the author.

Justin Zobel is Head of the University of Melbourne's Department of Computing & Information Systems. He received his PhD from the University of Melbourne and for many years was based at RMIT University, where he led the Search Engine group. As a researcher, Professor Zobel is best known for his role in the development of algorithms for efficient web search. His current research areas include search, measurement and evaluation, bioinformatics, fundamental algorithms and data structures and compression. He is an author of around 200 papers, has written three texts on postgraduate study and research methods and is an associate editor of ACM Transactions on Information Systems, Information Processing & Management, and Information Retrieval.

Bibliographic Information

Book Title : Writing for Computer Science

Authors : Justin Zobel

DOI : https://doi.org/10.1007/978-1-4471-6639-9

Publisher : Springer London

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer-Verlag London 2014

Softcover ISBN : 978-1-4471-6638-2 Published: 17 February 2015

eBook ISBN : 978-1-4471-6639-9 Published: 09 February 2015

Edition Number : 3

Number of Pages : XIII, 284

Number of Illustrations : 28 b/w illustrations

Topics : Popular Computer Science , Computer Science, general

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Penn State University Libraries

Computer science and engineering.

• Reference Sources
• Finding Articles and Databases
• Finding Books
• Finding Websites
• Penn State Resources and Organizations
• Books, Articles, and Other Educational Resources
• Research Tips
• Main Parts of a Scientific/Technical Paper
• Technical Writing Resources
• Ten Tips for Technical Writing
• Professional Organizations

Parts of a Technical Paper

The basic parts of a scientific or technical paper are:

Title and Author Information Abstract Introduction Literature Review Methods Results Discussion Conclusions References and Appendices

Detailed Explanation for Each Part

Title and Author Information:

The title of your paper and any needed information about yourself (usually your name and institution).

A short (usually around 250-400 words) description of the paper. Should include what the purpose of the paper is (including the basic research question/problem), the basic design of your project, and the major findings.

Introduction:

A general introduction to your topic and what you expect to learn from your project or experiment. Your research question should be found here.

Literature Review:

An analysis of what has already been published about your chosen topic. Should be able to show how your research question fits into the context of your field.

A description of everything you did in your experiment or project, step-by-step. Needs to be detailed enough so that any reader would be able to repeat each step exactly on their own.

What actually happened during your project or what you found at the end of your experiment. This is usually the best part to include the majority of your graphs, photos, tables, and other visual aids, as long as they help explain the results of your work.

Discussion:

An analysis of the results that integrates what you found into the wider body of research in your field. Can also include future hypotheses to be tested or future projects to build from your own.

Conclusion:

Can be included in the discussion if necessary. A final summary of the paper, including whether or not you were able to answer your original research question.

References and Appendices:

The reference page(s) is a list of all the sources you used to research and create your project/experiment, including everything cited in the literature review and methods sections. Remember to use the same citation style throughout the paper. An appendix would include any additional information about your work that you were not able to include within the body of your paper (like large datasets and figures) that would help readers better understand your results.

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

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Top 16 International Computer Science Journals — A Template Guide

MS Word, LaTeX Templates and Author Guidelines

This post is part of a series of blogs with links to Word, LaTeX templates and author instructions of top journals around the world in more than 25 core subjects in academia.

Getting started with your Research Paper Formatting

Writing a successful research paper is more than just communicating your knowledge . Most of the journals prescribe detailed set of authoring guidelines to apply on your content before you submit. Many research papers even get rejected for not following the guidelines of the journal (a reason why we built SciSpace (Formerly Typeset) — a platform to automatically apply 100% journal guidelines on your content).

To get you quickly started with your research paper formatting, this blog article lists journal formats and authoring guidelines of top international journals in Computer Science. You can find the links to MS Word template as well as LaTeX template of each of the journal here. You can also find the access link to the detailed author guidelines set by the journal. Feel free to check it out, share with friends and comment on the article.

Top International Computer Science Journals

We have used "Impact Factor" and various other parameters to rank the journals( Source ).

1. IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. It covers research in computer vision and image understanding, pattern analysis and recognition, and machine intelligence. machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.

Impact Factor — 5.694 (2013)

Journal Abbreviation — IEEE Trans. Pattern Anal. Mach. Intell.

Download MS Word Template here

Download LaTeX Template here

Check out the detailed Author Guidelines here

2. Artificial Intelligence

Artificial Intelligence is a scientific journal on artificial intelligence research. It was established in 1970 and is published by Elsevier.

Impact Factor — 3.333 (2015)

Find instructions for MS Word Template here

** The journal doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a few click s .

3. Communications of the ACM

Communications of the ACM is the monthly Journal of the Association for Computing Machinery (ACM). The focus is on the practical implications of advances in information technology and associated management issues; ACM also publishes a variety of more theoretical journals.

Impact Factor — 3.301 (2015)

Journal Abbreviation — Commun ACM

** The journal doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click.

4. Computer

Computer is an IEEE Computer Society practitioner-oriented magazine and contains peer-reviewed articles, regular columns and interviews on current computing-related issues.

Impact Factor — 1.438 (2013)

5. IEEE Transactions on Computers

IEEE Transactions on Computers is a monthly peer-reviewed scientific journal covering all aspects of computer design. It was established in 1952 and is published by the IEEE Computer Society.

Impact Factor — 1.473 (2013)

Journal Abbreviation — IEEE Trans. Comput.

6. IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined.

Impact Factor — 5.545 (2013)

Journal Abbreviation — IEEE Trans. Evolut. Comput.

7. IEEE Transactions on Fuzzy Systems

IEEE Transactions on Fuzzy Systems is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design or applications of fuzzy systems ranging from hardware to software, including significant technical achievements, exploratory developments, or performance studies of fielded systems based on fuzzy models.

Impact Factor — 6.701 (2016)

Journal Abbreviation — IEEE Trans. Fuzzy Syst.

8. Journal of Cryptology

The Journal of Cryptology is a scientific journal in the field of cryptology and cryptography. The journal is published quarterly by the International Association for Cryptologic Research.

Impact Factor — 1.021(2015)

** The journal doesn’t provide you a LaTeX template .The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click and download the LaTeX version.

9. IEEE Transactions on Information Theory

IEEE Transactions on Information Theory is a monthly peer-reviewed scientific journal published by the IEEE Information Theory Society. It covers information theory and the mathematics of communications.

Impact Factor — 2.65 (2013)

Journal Abbreviation — IEEE Trans. Inf. Theory

Check out MS Word Template here

Check out LaTeX Template here

10. IEEE Transactions on Neural Networks and Learning Systems

IEEE Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design, and applications of neural networks and related learning systems.

Impact Factor — 4.37 (2013)

Journal Abbreviation — IEEE Trans. Neural Netw. Learn. Syst

11. Journal of the ACM

The Journal of the ACM is a peer-reviewed scientific journal covering computer science in general, especially theoretical aspects. It is an official journal of the Association for Computing Machinery.

Impact Factor — 2.353 (2011)

Journal Abbreviation — J. ACM

12. Journal of Artificial Intelligence Research

The Journal of Artificial Intelligence Research is an open access peer-reviewed scientific journal covering research in all areas of artificial intelligence. Paper volumes are printed by the AAAI Press.

Impact Factor — 1.691 (2010)

Journal Abbreviation — J. Artif. Intell. Res

13. Journal of Functional Programming

The Journal of Functional Programming is a peer-reviewed scientific journal covering the design, implementation, and application of functional programming languages, spanning the range from mathematical theory to industrial practice.

Impact Factor — 1.357(2015)

** The journal doesn’t provide you a LaTeX template . The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace , format it to the journal guidelines in a click and download your document in LaTeX format.

14. International Journal of Computer Vision

The International Journal of Computer Vision (IJCV) is a journal published by Springer.

Impact Factor — 3.623 (2012)

Journal Abbreviation — IJCV

Find LaTeX instructions here

Find MS Word instructions here

** The journal doesn’t provide you a LaTeX template . The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click.

15. Journal of Machine Learning Research

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning.

Impact Factor — 2.45(2015)

16. SIAM Journal on Computing (SICOMP)

The SIAM Journal on Computing ( SICOMP ) is a scientific journal focusing on the mathematical and formal aspects of computer science. It is published by the Society for Industrial and Applied Mathematics (SIAM).

Journal Abbreviation — SIAM J. Comput.

If you found this list useful, please do share top computer science journals’ templates with your fellow researchers, academics and colleagues.

A research writing tool that helps you follow 100% guidelines

Adhering to fuzzy journal guidelines that runs to hundreds of pages is every researcher’s nightmare. That’s where SciSpace comes in.

SciSpace has around 14000 journal templates and enables you to format or re-format your research paper to all of the journal guidelines with 100% accuracy. What more, you save loads of your time while doing it .

SciSpace also has various University thesis, assignments and top international Conferences’ templates. Check it out here .

Before you go, SciSpace may be of interest if you are trying to simplify their research workflows. Discover, write, and collaborate on research papers using this comprehensive end-to-end solution. Your scholarly output can be displayed and managed seamlessly with the tools and resources we provide.

By combining writing and publishing tools, copyright detection technology, and searchable indexing, this platform will let you display and manage your scholarly output.It is a good idea to make notes about your citations while conducting a literature review. With SciSpace Discover, it is easy to cite your sources. Using the citation button on an article page, you can generate citation text that is preloaded in multiple formats, so you can copy and paste as you need.

Related Reading

• Top 11 international Biology journals — a template guide
• Top 13 international Material Science journals- a template guide
• Template collection of top Chemistry journals
• How SciSpace simplifies your research writing

You might also like

Top 12 Elsevier Journals in 2022

The A-Z of Open Journal Systems — Chapter 3

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Tips for writing a good quality Computer Science Research Paper

Before you start writing a good quality computer science research paper, let us first understand what one is. A computer science research paper is a paper written by professionals, scholars and scientists, who are strongly associated with computer science and information technology in general, which may be a research study. If you are novel to this field, then you can consult with your supervisor or guide.

Techniques for writing a good quality computer science research paper:

Choosing the topic: In most cases, the topic is selected by the interests of the author, but it can also be suggested by the guides. You can have several topics, and then judge which you are most comfortable with. This may be done by asking several questions of yourself, like "Will I be able to carry out a search in this area? Will I find all necessary resources to accomplish the search? Will I be able to find all information in this field area?" If the answer to this type of question is "yes," then you ought to choose that topic. In most cases, you may have to conduct surveys and visit several places. Also, you might have to do a lot of work to find all the rises and falls of the various data on that subject. Sometimes, detailed information plays a vital role, instead of short information. Evaluators are human: The first thing to remember is that evaluators are also human beings. They are not only meant for rejecting a paper. They are here to evaluate your paper. So present your best aspect.

Think like evaluators: If you are in confusion or getting demotivated because your paper may not be accepted by the evaluators, then think, and try to evaluate your paper like an evaluator. Try to understand what an evaluator wants in your research paper, and you will automatically have your answer. Make blueprints of paper: The outline is the plan or framework that will help you to arrange your thoughts. It will make your paper logical. But remember that all points of your outline must be related to the topic you have chosen.

Ask your guides: If you are having any difficulty with your research, then do not hesitate to share your difficulty with your guide (if you have one). They will surely help you out and resolve your doubts. If you can't clarify what exactly you require for your work, then ask your supervisor to help you with an alternative. He or she might also provide you with a list of essential readings.

Use of computer is recommended: As you are doing research in the field of computer science then this point is quite obvious. Use right software: Always use good quality software packages. If you are not capable of judging good software, then you can lose the quality of your paper unknowingly. There are various programs available to help you which you can get through the internet.

Use the internet for help: An excellent start for your paper is using Google. It is a wondrous search engine, where you can have your doubts resolved. You may also read some answers for the frequent question of how to write your research paper or find a model research paper. You can download books from the internet. If you have all the required books, place importance on reading, selecting, and analyzing the specified information. Then sketch out your research paper. Use big pictures: You may use encyclopedias like Wikipedia to get pictures with the best resolution. At Global Journals, you should strictly follow here

Bookmarks are useful: When you read any book or magazine, you generally use bookmarks, right? It is a good habit which helps to not lose your continuity. You should always use bookmarks while searching on the internet also, which will make your search easier.

Revise what you wrote: When you write anything, always read it, summarize it, and then finalize it.

Make every effort: Make every effort to mention what you are going to write in your paper. That means always have a good start. Try to mention everything in the introduction—what is the need for a particular research paper. Polish your work with good writing skills and always give an evaluator what he wants. Make backups: When you are going to do any important thing like making a research paper, you should always have backup copies of it either on your computer or on paper. This protects you from losing any portion of your important data.

Produce good diagrams of your own: Always try to include good charts or diagrams in your paper to improve quality. Using several unnecessary diagrams will degrade the quality of your paper by creating a hodgepodge. So always try to include diagrams which were made by you to improve the readability of your paper. Use of direct quotes: When you do research relevant to literature, history, or current affairs, then use of quotes becomes essential, but if the study is relevant to science, use of quotes is not preferable.

Use proper verb tense: Use proper verb tenses in your paper. Use past tense to present those events that have happened. Use present tense to indicate events that are going on. Use future tense to indicate events that will happen in the future. Use of wrong tenses will confuse the evaluator. Avoid sentences that are incomplete.

Pick a good study spot: Always try to pick a spot for your research which is quiet. Not every spot is good for studying.

Know what you know: Always try to know what you know by making objectives, otherwise you will be confused and unable to achieve your target.

Use good grammar: Always use good grammar and words that will have a positive impact on the evaluator; use of good vocabulary does not mean using tough words which the evaluator has to find in a dictionary. Do not fragment sentences. Eliminate one-word sentences. Do not ever use a big word when a smaller one would suffice.

Verbs have to be in agreement with their subjects. In a research paper, do not start sentences with conjunctions or finish them with prepositions. When writing formally, it is advisable to never split an infinitive because someone will (wrongly) complain. Avoid clichés like a disease. Always shun irritating alliteration. Use language which is simple and straightforward. Put together a neat summary.

Arrangement of information: Each section of the main body should start with an opening sentence, and there should be a changeover at the end of the section. Give only valid and powerful arguments for your topic. You may also maintain your arguments with records.

Never start at the last minute: Always allow enough time for research work. Leaving everything to the last minute will degrade your paper and spoil your work.

Multitasking in research is not good: Doing several things at the same time is a bad habit in the case of research activity. Research is an area where everything has a particular time slot. Divide your research work into parts, and do a particular part in a particular time slot.

Never copy others' work: Never copy others' work and give it your name because if the evaluator has seen it anywhere, you will be in trouble. Take proper rest and food: No matter how many hours you spend on your research activity, if you are not taking care of your health, then all your efforts will have been in vain. For quality research, take proper rest and food.

Go to seminars: Attend seminars if the topic is relevant to your research area. Utilize all your resources.

Refresh your mind after intervals: Try to give your mind a rest by listening to soft music or sleeping in intervals. This will also improve your memory. Acquire colleagues: Always try to acquire colleagues. No matter how sharp you are, if you acquire colleagues, they can give you ideas which will be helpful to your research.

Think technically: Always think technically. If anything happens, search for its reasons, benefits, and demerits. Think and then print: When you go to print your paper, check that tables are not split, headings are not detached from their descriptions, and page sequence is maintained.

Adding unnecessary information: Do not add unnecessary information like "I have used MS Excel to draw graphs." Irrelevant and inappropriate material is superfluous. Foreign terminology and phrases are not apropos. One should never take a broad view. Analogy is like feathers on a snake. Use words properly, regardless of how others use them. Remove quotations. Puns are for kids, not grunt readers. Never oversimplify: When adding material to your research paper, never go for oversimplification; this will definitely irritate the evaluator. Be specific. Never use rhythmic redundancies. Contractions shouldn't be used in a research paper. Comparisons are as terrible as clichés. Give up ampersands, abbreviations, and so on. Remove commas that are not necessary. Parenthetical words should be between brackets or commas. Understatement is always the best way to put forward earth-shaking thoughts. Give a detailed literary review.

Report concluded results: Use concluded results. From raw data, filter the results, and then conclude your studies based on measurements and observations taken. An appropriate number of decimal places should be used. Parenthetical remarks are prohibited here. Proofread carefully at the final stage. At the end, give an outline to your arguments. Spot perspectives of further study of the subject. Justify your conclusion at the bottom sufficiently, which will probably include examples.

Upon conclusion: Once you have concluded your research, the next most important step is to present your findings. Presentation is extremely important as it is the definite medium though which your research is going to be in print for the rest of the crowd. Care should be taken to categorize your thoughts well and present them in a logical and neat manner. A good quality research paper format is essential because it serves to highlight your research paper and bring to light all necessary aspects of your research.

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Open Access

Ten simple rules for writing a paper about scientific software

* E-mail: [email protected]

Affiliations Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Epidemiology, Biostatistics, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Center of Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

• Joseph D. Romano, 
• Jason H. Moore

Published: November 12, 2020

• https://doi.org/10.1371/journal.pcbi.1008390
• Reader Comments

Papers describing software are an important part of computational fields of scientific research. These “software papers” are unique in a number of ways, and they require special consideration to improve their impact on the scientific community and their efficacy at conveying important information. Here, we discuss 10 specific rules for writing software papers, covering some of the different scenarios and publication types that might be encountered, and important questions from which all computational researchers would benefit by asking along the way.

Author summary

Computational researchers have a responsibility to ensure that the software they write stands up to the same scientific scrutiny as traditional research studies. These 10 simple rules make doing so easier by enhancing usability, reproducibility, transparency, and other crucial characteristics that aren’t taught in most computer science or research methods curricula.

Citation: Romano JD, Moore JH (2020) Ten simple rules for writing a paper about scientific software. PLoS Comput Biol 16(11): e1008390. https://doi.org/10.1371/journal.pcbi.1008390

Editor: Scott Markel, Dassault Systemes BIOVIA, UNITED STATES

Copyright: © 2020 Romano, Moore. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work is funded with support from NIH grants R01LM010098, R01LM012601, R01AI116794, UL1TR001878, UC4DK112217 (PI: Jason Moore), and P30ES013508 (PI: Trevor Penning). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent decades, scientific software has become a critical feature of virtually all research workflows [ 1 ]. Computational researchers and informaticians, therefore, have a responsibility to release and disseminate software in the same scientifically rigorous manner as other research protocols, datasets, and empirical studies released into the scientific community. Writing (and publishing) a peer-reviewed paper about a newly developed scientific software is arguably one of the best ways to do this—“software papers” can reach a massive number of potential users (even acting as advertisement for the software), are a great way to show that the software stands up to scientific scrutiny, and allow users to easily reuse and cite the software in their own research.

However, software papers are fundamentally different from other “traditional” research articles. The process of designing and implementing software is different from designing and carrying out bench experiments, clinical studies, or raw data analyses [ 2 ]. There are also differences in the “final product” of the research: Software studies, obviously, yield a piece of software to be directly reused, whereas other study paradigms provide new protocols, specific findings, and follow-up questions or hypotheses.

There are basically 2 types of software papers: (1) stand-alone papers that solely describe the software, usually in a shorter format than an article written about a traditional research study; and (2) a (more traditional) article describing an original research question that includes development of a piece of new software as one of its critical components. Examples of the former include the original papers describing Biopython [ 3 ], scikit-learn [ 4 ], and SAMtools [ 5 ]. The latter includes the papers that introduced Gene Set Enrichment Analysis [ 6 ], the Connectivity Map tools [ 7 ], and VIPER [ 8 ]. Although these options produce 2 very different styles of paper, the 10 simple rules presented below largely apply to both of them.

Rule 1: Read the other “Ten Simple Rules” papers on coding

In order to have a good software paper, you first need to have good software. All of the other rules for writing great scientific software apply here, especially those that are already covered in other “Ten Simple Rules” articles. All impactful scientific software should aim to be robust [ 9 ], well documented [ 10 ], easy to use [ 11 ], and maintained under version control [ 12 ]. The advantages of making your software open source (with transparent licensing terms) and hosted on public repositories are widely acknowledged [ 13 ] and should be practiced regularly, unless there is a compelling reason not to. Evaluations, use cases, and demonstrative examples should make use of high-quality data that is ideally already well characterized [ 14 , 15 ].

Rule 2: Know the most appropriate publication venues and submission types

Journals and conference proceedings that focus on computational areas of research frequently have article types that are dedicated specifically to descriptions of new software or databases, and these can be a great venue for disseminating information quickly, concisely, and to an audience with an assumed level of technical proficiency. It’s good to think early and reconsider often when finding a specific journal or conference. Make sure to pay special attention to any nonstandard requirements that journals impose—some require evaluations to use real (i.e., nonsynthetic) data [ 16 ], others have special reporting or data/software deposition requirements [ 17 ], and you should always consider whether your software (and desired paper style) match the mission statement and/or goals of the journal or meeting. Discussions with mentors, collaborators, and other colleagues can be hugely beneficial in this context; their past successes and failures can end up saving you from submitting to an unsuitable journal (and all of the headaches that come with it). Some examples of popular submission types and journals for software papers include Bioinformatics (submission type: “original papers” or “application notes”), Nucleic Acid Research’s yearly “Database issue” and “Web Server issue,” and PLOS Computational Biology’s “software articles.”

Rule 3: Publish for users, not developers

In spite of Rule 2, you should always consider submission to noncomputational venues. As computational researchers, we often work in highly interdisciplinary areas, writing software that makes research in other fields easier, more efficient, and more scientifically robust. Scientists working in these fields are often extremely interested in hearing about new software tools that will help them on a daily basis, but they may not frequently search computationally focused journals or conference proceedings. Especially if the software is meant to be easily accessible to bench scientists or other noncomputational stakeholders, describing your software in a domain-specific journal is an excellent way to reach a wider audience. Furthermore, a paper describing an innovative new software tool in one of these journals has a great chance of standing out in comparison to other articles, especially when the field has highlighted a need for new software approaches to long-standing challenges.

However, a few things need to be kept in mind, especially when publishing in a noncomputational journal or conference. If software papers are uncommon in your field, there may not be an ideally suitable publication/article type, and you may need to be creative in how you organize your presentation of the software and your evaluation of its performance. Specifically, think of how your software can address a particular limitation or research question of interest to the field, and show an example demonstrating that it can do so. This can become a primary focus of the paper, or it can be one of several shorter “case studies” that show off useful functionality. Think about the story you want to tell, and what your target audience would find the most useful. Similarly, reviewers might be unfamiliar with how to assess and critique software papers. When in doubt, it never hurts to contact a journal editor or program committee member for guidance—they might even be able to direct the article to a set of reviewers they know have the needed technical expertise. If the publication venue asks authors for reviewer suggestions, you should be able to come up with a similar set yourself. You should also keep your readers in mind: If most of your intended audience have limited computational experience, you should actively cut down on jargon and technical details. These details can be added as supplemental data, published separately as a nontraditional article (e.g., via Zenodo, F1000, or similar), or even be moved entirely to online documentation (see Rule 6).

Good illustrative examples of software papers published in noncomputational journals are plentiful. Many older software papers were published in domain-specific journals, since most of the interdisciplinary fields that eventually led to computationally focused journals were still emerging. This can be seen, for example, in computational phylogenetics and cladistics, a field that began as early as the 1970s [ 18 , 19 ]. More modern examples of highly impactful software papers in domain-specific journals are also plentiful, like those introducing PLINK [ 20 ] and Circos [ 21 ].

Rule 4: Create a long-term software management plan

In academia, affiliations, funding sources, and technology infrastructures change frequently. Researchers therefore assume a level of responsibility for keeping the products of our research available to the rest of the scientific community when things do change. When you release a new piece of software or body of code, you should establish guidelines to help ensure its persistence—otherwise, your papers, and those of others that rely on the software, will be negatively impacted. These guidelines form what can be thought of as a “software management plan” [ 22 , 23 ]. To create one, it can be helpful to ask yourself and your coauthors the following questions:

• Who is responsible for maintaining the software in the future, should affiliations change? The first author on the paper, the lab’s principal investigator (PI), or someone else?
• What is the cost (if any) of keeping the software and any related resources—relevant databases, web apps, application programming interfaces (APIs), etc.—online? What is the funding source? How will it be funded if this source is exhausted?
• Who owns the intellectual property (IP) behind the software? This is often the institution or company that employs the paper’s PI, but it may be different, and it may affect how the software is maintained in the long term. Furthermore, it is crucial to know how ownership of the IP will affect licensing [ 24 ]. Generally, it’s good practice to adopt the most permissive license that doesn’t violate ownership or usage/privacy policies.
• Will updates and bug fixes be provided? If the updates are major, will follow-up papers be published (see Rule 9)? Are any regular maintenance activities necessary, and if so, who will perform them?
• What will happen to the software if data or other resources it relies upon are no longer available?
• When and how will you archive the software? Online code repositories (e.g., via GitHub) make doing so easy, and tools like Zenodo and FigShare let you tie permanent DOIs to specific archives (see Rule 5).

Generally, software management plans aren’t outlined in the actual body of software papers, but an idea of how the lifecycle of the software will be handled—along with general policies and strategies for maintaining the software—are often included in online code repositories, such as in “Contributing” guides or the software’s README (e.g., [ 25 – 27 ]). General tips and guides on developing software management plans are in ample supply online [ 22 , 23 ].

Rule 5: Safeguard against “link rot”

As papers age, it’s unfortunately quite common for hyperlinks to permanently break—the resource they point to has moved, has been taken offline, or affected by some other internet-related issue. This is known as “link rot,” and it is not just contained to academic articles—link rot can affect blogs, social media posts, web pages, and other digital resources [ 28 ]. However, it is especially prevalent in scientific articles—a 2013 study by Hennessey and Ge found that the median uniform resource locator (URL) lifespan is 9.3 years, with some falling far shorter than that mark [ 29 ]. While blogs, README documents, and source code can be edited to point to new links, peer-reviewed papers are static—unless you issue a correction or erratum, the URL you use at the time of publication is the URL that will be in that paper permanently.

Several relatively easy steps can be taken to prevent link rot in papers about scientific software. Institutional affiliations and website structure can change (as mentioned in Rule 4), so it is best to host web apps, APIs, software descriptions, example code, and other digital resources either on a dedicated domain, an independently hosted lab website, or on a free web hosting resource (e.g., using GitHub Pages). However, be familiar with how the host handles persistent links. For example, links on GitHub Pages sites can break if a repository is transferred to a new owner. When digital resources do need to move to a new URL, you should make an effort to set up URL redirection from the old location to the new location, which can usually be arranged with web server administrators. You should also set up persistent versioned releases of software and assign separate DOIs to point to the current software release at the time of the paper’s publication. Zenodo (for software releases tagged on GitHub) and FigShare (for data files, scripts, and other digital resources) are free services that track permanently archived research materials and assign DOIs that basically “solve” link rot when used effectively. Also, having a well-documented system for assigning meaningful URLs to individual resources can help to diagnose the cause if links do break. For example, “http://<domain>/protein/BRCA1” is likely far better than “http://<domain>/540/65df7.php?id=18427,” both from a usability and a maintenance perspective.

Rule 6: Make a clear distinction between code documentation and research results

Whenever software is intended for reuse, high-quality documentation is crucial. However, peer-reviewed papers are arguably not where documentation should be presented. The paper should describe the software (including the design process, technical details, and algorithmic innovations) and any accompanying analyses. Any time you include code in the paper, you’re making a commitment to support the syntax and semantics in the code. Since it’ll be permanently visible to scientific users, changes that break the example code will likely lead to confusion and potentially result in alienating the users. If it’s especially important to show usage examples or other instructions on how to use the software, and they occupy more than a small handful of sentences, they should either (1) be moved to an appendix or supplemental materials document; or (2) be placed in the code’s documentation. If you have dedicated documentation pages online, it’s a great idea to provide a link to those pages in the body of the paper. To ensure consistency, the documentation should also be version controlled, and the link in the paper should point to the version of the documentation that is current at the time of writing. As a side note, sample input/output and example code that support results presented in the paper can also be placed in the software’s version control system and even integrated into the software’s test suite as acceptance tests [ 30 ].

Rule 7: Be current with modern tooling and best coding practices

Many of the choices you make in the development of your software itself can have a profound effect on the longevity and scientific impact of the paper that describes it. If the software solves an important unmet need, yet is challenging to install, written in an obsolete programming language, and filled with bugs, it probably will not attain widespread use. Similarly, the paper would stand a high likelihood of falling by the wayside, if it even passes peer review. Fortunately, a relatively small amount of advance planning during the early stages of development can help avoid this particular issue. We find that the following guidelines are helpful here:

• Use a well-maintained programming language that runs on most modern systems.
• Publish your software on at least 1 packaging index so that users can install it with a single command. Using a continuous integration (CI) service can automate this process and reduce the likelihood of human error [ 31 ].
• Similarly, if possible, distribute your software both as raw source code and as a packaged or compiled version.
• As mentioned before, provide detailed documentation and instructions for use.
• If possible, provide ways for users to contribute to future development, especially in terms of bug fixes and requested features.

Doing so is also important for more fundamentally pragmatic reasons: A good way to encourage widespread use of software is to make it easy to install and use and to give it a fresh, modern look. Although this is not directly related to the scientific quality of the software or the paper, dissemination of research and research tools is an important part of the scientific process and should always be given special thought.

Rule 8: Maintain consistency between code, documentation, websites, and papers

By its very nature, any software paper needs to manage references to (and between) an ecosystem of digital resources describing the software, including websites, source code repositories, documentation, example code, blog articles, and other media types, all of which refer to the same piece of software. Make sure to maintain consistency across this ecosystem. Use the same spelling, punctuation, and capitalization in any names you make up for your software. If you create a logo for the software, use it in multiple places. Make heavy use of links between different sites and resources so readers can find what they are looking for quickly and easily. An easy trick for ensuring version consistency is to include version numbers directly in URLs, where appropriate. For example, documentation pages might be given the URL “http://<domain>/version1/doc.” If the software is part of a larger body of research that has produced other pieces software, it might be a good idea to establish a naming system to indicate the relationship, while ensuring that each can be easily referred to without ambiguity. Don’t force acronyms in your naming either—keep acronyms simple or avoid them entirely.

Rule 9: Plan for follow-up publications and update the software accordingly

More often than not, software development does not end after its first major release—rather, developers add new features and respond to bugs or other performance issues. This definitely applies to scientific software, too, which stems largely from the fact that good research is usually iterative [ 13 ] and conducted in stages that are either planned from the outset or guided by the successes and failures of earlier steps. Writing several papers along the way is more than a ploy to inflate citation metrics or boost a curriculum vitae (CV); it is a demonstration of rigorously following the scientific process, and it allows you to rapidly disseminate new findings to the community.

However, it is also important to know when it’s appropriate to write a follow-up paper. Things like bug fixes or minor usability enhancements are better suited for blog posts, version release notes, or message board/issue tracker threads. Discuss whether a new update constitutes a new scientific advancement and if that advancement solves a need that your user base currently faces. Generally, we aim to publish a new paper for every new major feature that is associated with a specific outstanding research question. For example, an ongoing project within our research group is the development of the Tree-based Pipeline Optimization Tool (TPOT)—an automated machine learning tool that uses genetic programming to automatically find machine learning pipelines that perform well on a given dataset [ 32 ]. In addition to the original publication describing TPOT, we have written follow-up papers for several major additions to the software, including new ways to specify pipeline templates [ 33 ] and support for deep learning [ 34 ].

Rule 10: Prioritize visibility and availability

There is a frustrating scenario that plays out often when performing computational research: You find a paper describing a piece of free, publicly available software that perfectly solves a problem you have been struggling with for several weeks. The paper is a bit old, but the methods seem elegant and robust. However, after finally tracking down a copy of the software, you find that there is no way to make it run on any modern operating system. You spend a few hours trying to track down its (apparently nonexistent) documentation, and eventually give up, deciding that it is either impossible to get the software running or that it will simply take less time to implement it yourself. Problems like these can’t be entirely avoided—software ages, programming languages eventually fall out of favor, and dependencies change in ways that you as a user cannot fully control. However, as a developer, you can take steps to effectively prolong your software’s life, and some of these steps can be implemented directly in the paper that describes the software.

First, redundancy does not hurt. If you have a main informational website for the software, include a link in the paper, as well as on the source repository, in the documentation, and on lab and institutional websites. Make use of social media to promote your work and encourage coauthors to do the same [ 35 ]. A popular metric for determining the social impact of an article is the Altmetric Attention Score [ 36 ], which uses not only citation count but also things like social media mentions, news coverage, and representation in popular science publications. “Badge icons” (sometimes known as “shields”) used on websites, code repositories, and package indexes let you provide rich links to different parts of your software ecosystem (including the paper itself via DOIs) that are dynamic, informationally dense, and visually appealing. Finally, both your software paper and associated media related to the software can be optimized for search engines, which can dramatically increase their scientific impact [ 37 ].

Many of these apply in reverse, too. Once your paper is published (and prior to that, if you release a preprint), your websites and code repositories should point back to the paper, using its DOI when possible. It’s also helpful to explicitly instruct users how to cite your work and provide preformatted citations in several popular styles and/or B ib TEX/EndNote/etc. files that can be exported directly to citation management software.

Software papers are an important component of the scientific research ecosystem, benefiting users in many domains and with widely varying levels of computational expertise. Furthermore, academic publications (and their citations) currently provide the primary means by which scientific software developers and maintainers gain recognition for their work (fortunately, efforts are currently under way to change this—for example, [ 38 , 39 ] show how code contributions can be used as directly citable scholarly works). Following these 10 simple rules will help to ensure your software papers are easy to use, scientifically rigorous, and resistant to future changes in technology.

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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

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• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
• Machine Learning for Climate Change Prediction
• Big Data Analytics for Social Sciences
• Artificial Intelligence for Energy Management
• Virtual Reality for Tourism and Travel
• Cybersecurity for Smart Grids
• Machine Learning for Image Recognition
• Augmented Reality for Sports Training
• Natural Language Processing for Content Creation
• Cloud Computing for High-Performance Computing
• Artificial Intelligence for Personalized Medicine
• Virtual Reality for Architecture and Design
• Augmented Reality for Product Visualization
• Natural Language Processing for Language Translation
• Cybersecurity for Cloud Computing
• Artificial Intelligence for Supply Chain Optimization
• Blockchain Technology for Digital Voting Systems
• Virtual Reality for Job Training
• Augmented Reality for Retail Shopping
• Natural Language Processing for Sentiment Analysis in Customer Feedback
• Cloud Computing for Mobile Application Development
• Artificial Intelligence for Cybersecurity Threat Detection
• Blockchain Technology for Intellectual Property Protection
• Virtual Reality for Music Education
• Machine Learning for Financial Forecasting
• Augmented Reality for Medical Education
• Natural Language Processing for News Summarization
• Cybersecurity for Healthcare Data Protection
• Artificial Intelligence for Autonomous Robots
• Virtual Reality for Fitness and Health
• Machine Learning for Natural Language Understanding
• Augmented Reality for Museum Exhibits
• Natural Language Processing for Chatbot Personality Development
• Cloud Computing for Website Performance Optimization
• Artificial Intelligence for E-commerce Recommendation Systems
• Blockchain Technology for Supply Chain Traceability
• Virtual Reality for Military Training
• Augmented Reality for Advertising
• Natural Language Processing for Chatbot Conversation Management
• Cybersecurity for Cloud-Based Services
• Artificial Intelligence for Agricultural Management
• Blockchain Technology for Food Safety Assurance
• Virtual Reality for Historical Reenactments
• Machine Learning for Cybersecurity Incident Response.
• Secure Multiparty Computation
• Federated Learning
• Internet of Things Security
• Blockchain Scalability
• Quantum Computing Algorithms
• Explainable AI
• Data Privacy in the Age of Big Data
• Adversarial Machine Learning
• Deep Reinforcement Learning
• Online Learning and Streaming Algorithms
• Graph Neural Networks
• Automated Debugging and Fault Localization
• Mobile Application Development
• Software Engineering for Cloud Computing
• Cryptocurrency Security
• Edge Computing for Real-Time Applications
• Natural Language Generation
• Virtual and Augmented Reality
• Computational Biology and Bioinformatics
• Internet of Things Applications
• Robotics and Autonomous Systems
• Explainable Robotics
• 3D Printing and Additive Manufacturing
• Distributed Systems
• Parallel Computing
• Data Center Networking
• Data Mining and Knowledge Discovery
• Information Retrieval and Search Engines
• Network Security and Privacy
• Cloud Computing Security
• Data Analytics for Business Intelligence
• Neural Networks and Deep Learning
• Reinforcement Learning for Robotics
• Automated Planning and Scheduling
• Evolutionary Computation and Genetic Algorithms
• Formal Methods for Software Engineering
• Computational Complexity Theory
• Bio-inspired Computing
• Computer Vision for Object Recognition
• Automated Reasoning and Theorem Proving
• Natural Language Understanding
• Machine Learning for Healthcare
• Scalable Distributed Systems
• Sensor Networks and Internet of Things
• Smart Grids and Energy Systems
• Software Testing and Verification
• Web Application Security
• Wireless and Mobile Networks
• Computer Architecture and Hardware Design
• Digital Signal Processing
• Game Theory and Mechanism Design
• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
• Cloud computing and its benefits
• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to write a computer science paper

You can find several resources on how to write a scientific paper on the Internet. Some good ones of them are linked on my main guidance page on writing a thesis or paper . This page summarizes some core issues on that topic. These issues apply to writing a master’s or PhD thesis as well. However, the requirements to the contribution differ greatly between these kinds of publications as does the number of pages you are allowed to write (usually about 100 pages for a thesis and only about 8–10 pages for a full research paper).

Contribution

First and foremost decide on what precisely is the contribution of your paper over the state of the art. If you think you have several contributions, focus on the most important one. It may be that you can add one or two contributions as side topics, but in general you should focus on the most important one in order to keep your paper focussed. As a side note: If you think you have several contributions for a single paper, you should probably invest in researching state of the art and related work more thoroughly.

When you know your contribution, think of a good title and sketch a straight argumentation line (red line) from the problem over the solution to the proof. This will help you to formulate the abstract and keep your paper focussed.

Find a short and precise title for you paper exactly matching the content. It’s worth investing time into this matter as the title will be that part of the paper by which it will be referenced (in case it gets published).

The abstract is one of the most important parts of the paper. You have only a few seconds in order to catch the reader’s interest. A bad abstract may already move you towards the rejection side in the reviewer’s decision process.

In your abstract, establish the context and relevance of your paper, motivate the problem, briefly describe the solution, and present the results of your work. Ideally, use one (short) sentence for each of the previously mentioned content items in order to keep your abstract short. Overall, this should be a short summary of the whole content of your paper, including results. The reader should understand the core of your paper in just a few sentences.Therefore, pick the most important result and also state it in the abstract, e.g., “Results show that our algorithm improves performance by 12% compared to the state of the art.” See the abstract as a personal challenge for each of your papers.

Writing the abstract in advance helps in getting your paper focused. However, re-read the abstract after you finished the paper. You will generally rewrite it after that for improvement.

Paper structure

Generally, papers follow the structure given below:

• Introduction : introduces into the context of the paper and shows why your work is relevant.
• State of the art : discusses the current state of the art in the area of your paper and often leads to the problem motivation.
• Problem motivation : precisely points to the problem addressed by the paper. Sometimes, this part is already included in the introduction.
• Solution : describes how you solved the problem.
• Proof / Evaluation / Discussion : You have to prove that your solution solves the problem indeed. Depending on the paper, this will look different. In theory papers, you usually have a formal/mathematical proof while in empirical papers you usually present the analysis (quantitatively and qualitatively) of a prototype implementation. However, you should know best how to prove your work.
• Related work : briefly summarizes the work of others in the same area of your paper, e.g., addressing the same problem or having a similar solution to a potentially different problem. Moreover, set the related work in relation to your own work, describing what is similar and where the differences are. Please note that it is bad practice to make the work of others bad in order say that you do it better. Compare your work to the work of others in an objective/neutral way – and be honest!
• Future work : state your ideas of what you estimate could be done in the future in order to further improve on the addressed problem. You may also state further problem areas you identified to be researched in the future. This section is not mandatory, but may be useful for others in identifying interesting new research problems.
• Conclusion : concludes the paper by again pointing to your key message. In contrast to the abstract, you can build on the fact that the reader now knows the content of your paper.

These items do not necessarily have their own section and some of them might be combined, e.g., state of the art and problem motivation as part of the introduction, especially for shorter papers. However, the listed items should be present in your paper as they are usually necessary to understand your work and your contribution and a reviewer of a conference or journal will look for them.

The question of whether related work comes at the end or together with the state of the art section has to be answered for each paper individually. Sometimes it fits better at the beginning and sometimes better at the end. If I don’t need to build on the content of the related work section, I usually keep it at the end as it allows for better comparison of your own and related work, which is hard to do before the reader knows the content of your paper.

For the section headlines, try to be as specific as possible and don’t use the generic titles where possible, e.g., “The XYZ-Framework” is much better than just “Solution” as it gives your solution a name. Some of the sections such as “Related Work” or “Conclusion” will of course be named that generic way.