Writing Technical Articles

The IEEE affirms that authorship credit must be reserved for individuals who have met each of the following conditions: 1) made a significant intellectual contribution to the theoretical development, system or experimental design, prototype development, and/or the analysis and interpretation of data associated with the work contained in the manuscript, 2) contributed to drafting the article or reviewing and/or revising it for intellectual content, and 3) approved the final version of the manuscript, including references.

This has now moved to the IEEE PSPB Operations Manual, Section 8.2.1.

Abstract

• The abstract must not contain references, as it may be used without the main article. It is acceptable, although not common, to identify work by author, abbreviation or RFC number. (For example, "Our algorithm is based upon the work by Smith and Wesson.")
• Avoid use of "in this paper" in the abstract. What other paper would you be talking about here?
• Avoid general motivation in the abstract. You do not have to justify the importance of the Internet or explain what QoS is.
• Highlight not just the problem, but also the principal results. Many people read abstracts and then decide whether to bother with the rest of the paper.
• Since the abstract will be used by search engines, be sure that terms that identify your work are found there. In particular, the name of any protocol or system developed and the general area ("quality of service", "protocol verification", "service creation environment") should be contained in the abstract.
• Avoid equations and math. Exceptions: Your paper proposes E = m c ².

Introduction

• Avoid stock and cliche phrases such as "recent advances in XYZ" or anything alluding to the growth of the Internet.
• Be sure that the introduction lets the reader know what this paper is about, not just how important your general area of research is. Readers won't stick with you for three pages to find out what you are talking about.
• The introduction must motivate your work by pinpointing the problem you are addressing and then give an overview of your approach and/or contributions (and perhaps even a general description of your results). In this way, the intro sets up my expectations for the rest of your paper -- it provides the context, and a preview.
• Repeating the abstract in the introduction is a waste of space.
• Example bad introduction:

  Here at the institute for computer research, me and my colleagues have created the SUPERGP system and have applied it to several toy problems. We had previously fumbled with earlier versions of SUPERGPSYSTEM for a while. This system allows the programmer to easily try lots of parameters, and problems, but incorporates a special constraint system for parameter settings and LISP S-expression parenthesis counting.

  The search space of GP is large and many things we are thinking about putting into the supergpsystem will make this space much more colorful.

• A pretty good introduction, drawn from Eric Siegel's class:

  Many new domains for genetic programming require evolved programs to be executed for longer amounts of time. For example, it is beneficial to give evolved programs direct access to low-level data arrays, as in some approaches to signal processing \cite{teller5}, and protein segment classification \cite{handley,koza6}. This type of system automatically performs more problem-specific engineering than a system that accesses highly preprocessed data. However, evolved programs may require more time to execute, since they are solving a harder task.

  Previous or obvious approach:

  (Note that you can also have a related work section that gives more details about previous work.)) One way to control the execution time of evolved programs is to impose an absolute time limit. However, this is too constraining if some test cases require more processing time than others. To use computation time efficiently, evolved programs must take extra time when it is necessary to perform well, but also spend less time whenever possible.

  Approach/solution/contribution:

  The first sentence of a paragraph like this should say what the contribution is. Also gloss the results.

  In this chapter, we introduce a method that gives evolved programs the incentive to strategically allocate computation time among fitness cases. Specifically, with an aggregate computation time ceiling imposed over a series of fitness cases, evolved programs dynamically choose when to stop processing each fitness case. We present experiments that show that programs evolved using this form of fitness take less time per test case on average, with minimal damage to domain performance. We also discuss the implications of such a time constraint, as well as its differences from other approaches to {\it multiobjective problems}. The dynamic use of resources other than computation time, e.g., memory or fuel, may also result from placing an aggregate limit over a series of fitness cases.

  Overview:

  The following section surveys related work in both optimizing the execution time of evolved programs and evolution over Turing-complete representations. Next we introduce the game Tetris as a test problem. This is followed by a description of the aggregate computation time ceiling, and its application to Tetris in particular. We then present experimental results, discuss other current efforts with Tetris, and end with conclusions and future work.

Body of Paper

Hints and common mistakes

Bibliography

• Avoid use of et al. in a bibliography unless list is very long (five or more authors). The author subsumed into et al. may be your advisor or the reviewer... Note punctuation of et al..
• Internet drafts must be marked "work in progress". Make sure that they have been replaced by newer versions or RFCs. Any Internet Draft reference older than six months should automatically be suspicious since Internet Drafts expire after that time period.
• Book citations include publication years, but no ISBN number.
• It is now acceptable to include URLs to material, but it is probably bad form to include a URL pointing to the author's web page for papers published in IEEE and ACM publications, given the copyright situation. Use it for software and other non-library material. Avoid long URLs; it may be sufficient to point to the general page and let the reader find the material. General URLs are also less likely to change.
• Leave a space between first names and last name, i.e., "J. P. Doe", not "J.P.Doe".
• References such as

  John Doe, "Some paper on something", technical report.
  

  are useless. Cite the source (e.g., university), date and other identifying information.
• For conference papers, you MUST name the conference location, month and the full conference name, not just some abbreviation. Page numbers are acceptable, but no longer all that helpful for electronic publications. All of this information is readily available via the IEEE or ACM digital libraries.
• Check if Internet drafts have been published as RFCs or if there's a newer version.
• Having a citation

  Jane Doe, "Some random paper", to be published, 2003.
  

  is useless, as the paper has presumably been published by now. Google or the ACM or IEEE digital libraries will help you find it.

Acknowledgements

• Acknowledge your funding sources. Some sources have specific wording requirements and may prefer that the grant number is listed. The NSF requires text like "This work was supported by the National Science Foundation under grant EIA NN-NNNNN."
• Generally, anonymous reviewers don't get acknowledged, unless they really provided an exceptional level of feedback or insight. Rather than "We thank X for helping us with Y", you might vary this as "X helped with Y.".

Reporting Numerical Results and Simulations

In all but extended abstracts, numerical results and simulations should be reported in enough detail that the reader can duplicate the results. This should include all parameters used, indications of the number of samples that contributed to the analysis and any initial conditions, if relevant.

When presenting simulation results, provide insight into the statistical confidence. If at all possible, provide confidence intervals. If there's a "strange" behavior in the graph (e.g., a dip, peak or change in slope), this behavior either needs to be explained or reasons must be given why this is simply due to statistical aberration. In the latter case, gathering more samples is probably advised.

Figures should be chosen wisely. You can never lay out the whole parameter space, so provide insight into which parameters are significant over what range and which ones are less important. It's not very entertaining to present lots of flat or linear lines.

The figure and table captions should contain enough context so that a reader can understand the content of the figure or table without having to refer to the text. Any labels or uncommon abbreviations need to be explained in the figure or table caption.

The description of the graph should not just repeat the graphically obvious such as "the delay rises with the load", but explain, for example, how this increase relates to the load increase. Is it linear? Does it follow some well-known other system behaviors such as standard queueing systems?

LaTeX Considerations

• There's no need to enclose numbers in $$ (math mode).
• Use \cite{a,b,c}, not \cite{a} \cite{b} \cite{c}.
• Use the \usepackage{times} option for LaTeX2e - it comes out much nicer on printers with different resolutions. Plus, compared to cmr, it probably squeezes an extra 10% of text out of your conference allotment.
• Non-index (or descriptive) subscripts are set in roman, not italic. For example,

  x_i
  x_{\mathit index}
  x_{\mathrm max}
  x_{\mathrm f}
  

  In the examples above, the index i or index is substituted or instantiated by numbers, such as x₁, x₂.

• Multi-letter variable names should be surrounded by

  \mathit

  , not $$, as otherwise the spacing will be wrong.
• For uniformity, use the LaTeX2e graphics set, not the earlier psfigure set:

  \usepackage{graphics}
  ...
  \begin{figure}
  \resizebox{!}{0.5\textheight}{\includegraphics{foo.eps}}
  \caption{Some figure}
  \label{fig:figure}
  \end{figure}
  

Things to Avoid

Too much motivational material

Three reasons are enough -- and they should be described very briefly.

Describing the obvious parts of the result

"Obvious" is defined as any result that a graduate of our program would suggest as a solution if you pose the problem that the result solves.

Describing unnecessary details

A detail is unnecessary, if its omission will not harm the reader's ability to understand the important novel aspects of the result.

Spelling errors

With the availability of spell checkers, there is no reason to have spelling errors in a manuscript. If you as the author didn't take the time to spell-check your paper, why should the editor or reviewer take the time to read it or trust that your diligence in technical matters is any higher than your diligence in presentation? Note, however, that spell checkers don't catch all common errors, in particular word duplication ("the the"). If in doubt, consult a dictionary such as the (on line) Merriam Webster.

Text in Arial:

Arial and other sans-serif fonts are fine for slides and posters, but are harder to read in continuous text. Use Times Roman or similar serif fonts. Unusual fonts are less likely to be available at the recipient and may cause printing or display problems. Material mainly to be read online typically use sans serif fonts.

Guidelines for Experimental Papers

"Guidelines for Experimental Papers" set forth for researchers submitting articles to the journal, Machine Learning.

1. Papers that introduce a new learning "setting" or type of application should justify the relevance and importance of this setting, for example, based on its utility in applications, its appropriateness as a model of human or animal learning, or its importance in addressing fundamental questions in machine learning.
2. Papers describing a new algorithm should be clear, precise, and written in a way that allows the reader to compare the algorithm to other algorithms. For example, most learning algorithms can be viewed as optimizing (at least approximately) some measure of performance. A good way to describe a new algorithm is to make this performance measure explicit. Another useful way of describing an algorithm is to define the space of hypotheses that it searches when optimizing the performance measure.
3. Papers introducing a new algorithm should conduct experiments comparing it to state-of-the-art algorithms for the same or similar problems. Where possible, performance should also be compared against an absolute standard of ideal performance. Performance should also be compared against a naive standard (e.g., random guessing, guessing the most common class, etc.) as well. Unusual performance criteria should be carefully defined and justified.
4. All experiments must include measures of uncertainty of the conclusions. These typically take the form of confidence intervals, statistical tests, or estimates of standard error. Proper experimental methodology should be employed. For example, if "test sets" are used to measure generalization performance, no information from the test set should be available to the learning process.
5. Descriptions of the software and data sufficient to replicate the experiments must be included in the paper. Once the paper has appeared in Machine Learning, authors are strongly urged to make the data used in experiments available to other scientists wishing to replicate the experiments. An excellent way to achieve this is to deposit the data sets at the Irvine Repository of Machine Learning Databases. Another good option is to add your data sets to the DELVE benchmark collection at the University of Toronto. For proprietary data sets, authors are encouraged to develop synthetic data sets having the same statistical properties. These synthetic data sets can then be made freely available.
6. Conclusions drawn from a series of experimental runs should be clearly stated. Graphical display of experimental data can be very effective. Supporting tables of exact numerical results from experiments should be provided in an appendix.
7. Limitations of the algorithm should be described in detail. Interesting cases where an algorithm fails are important in clarifying the range of applicability of an algorithm.

Other Hints and Notes

From Bill Stewart (Slashdot, May 7, 2006), edited

• Write like a newspaper reporter, not a grad student.
• Your objective is clear communication to the reader, not beauty or eruditeness or narration of your discoveries and reasoning process. Don't waste their time, or at least don't waste it up front.
• Hit the important conclusions in the first few sentences so your reader will read them. If you'd like to wrap up with them at the end of your memo, that's fine too, in case anybody's still reading by then, but conclusions come first.
• If you're trying to express something complex, simplify your writing so it doesn't get in the way. For something simple, 10th grade language structures will do, but if it's really hairy stuff, back down to 8th grade or so.
• Think about what your audience knows and doesn't know, and what they want and don't want. Express things in terms of what they know and want, not what you know.

From MarkusQ, Slashdot, May 7, 2006

• Top down design Starting with an outline and working out the details is the normal way of tackling an engineering problem.
• Checking your facts Engineers should be used to checking anything that is even remotely doubtful before committing to it. So should writers.
• Failure mode analysis For each sentence ask yourself, could it be misread? How? What is the best way to fix it?
• Dependency analysis Are the ideas presented in an order that assures that each point can be understood on the basis of the readers assumed knowledge and the information provided by preceding points?
• Optimization Are there any unnecessary parts? Does the structure require the reader to remember too many details at once, before linking them?
• Structured testing If you read what you have written assuming only the knowledge that the reader can be expected to have, does each part work the way you intended? If you read it aloud, does it sound the way you intended?

The Conference Review Process

It is hard to generalize the review process for conferences, but most reputable conferences operate according to these basic rules:

1. The paper is submitted to the technical program chair(s). All current conferences require electronic submission, in PDF, occasionally in Word.
2. The technical program chair assigns the paper to one or more technical program committee members, hopefully experts in their field. The identity of this TPC member is kept secret.
3. The TPC member usually provides a review, but may also be asked to find between one and three reviewers who are not members of the TPC. They may be colleagues of the reviewer at the same institution, his or her graduate students or somebody listed in the references. The graduate student reviews can be quite helpful, since these reviewers often provide more detailed criticism rather than blanket dismissal. Any good conference will strive to provide at least three reviews, however, since conferences operate under tight deadlines and not all reviewers deliver as promised, it is not uncommon that you receive only two reviews.
4. In some conferences, there is an on-line discussion of papers among the reviewers for a particular paper. Usually, a lead TPC member drives the discussion and then recommends the paper for acceptance, rejection or discussion at the TPC meeting.
5. The technical program chair then collects the reviews and sorts the papers according to their average review scores.
6. The TPC (or, rather, the subset that can make the meeting), then meets in person or by phone conference. Usually, the bottom third and the top third are rejected and accepted, respectively, without (much) further discussion. The papers discussed are those in the middle of the range, or where a TPC member feels strongly that the paper ended up in the wrong bin, or where the review scores differ significantly. Papers that only received two reviews are also often discussed, maybe with a quick review by one of the TPC members as additional background. The rigor of the TPC meeting depends on the size and reputation of the conference. In some workshops and conferences, the TPC chairs may well make the final decision themselves, without involving the whole TPC.

Other References

• A series of short YouTube videos by Simone Silvestri: How to write an abstract, an introduction, a related work section; how to comply with a double blind review policy

This is an amazing little book that you can read in a few hours. Your writing style will never be the same afterwards. This $8 book is the best investment you can ever make.

This is a great book that expands on Strunk&White. It has more examples, and was written by an author who edited numerous technical publications, many of which were in computer science.

This is the bible for American academic style. It's long and heavy, but has everything you ever want to know about style. When in doubt, or if you get conflicting stylistic advice, following The Chicago Manual of Style is your best choice.

This is another useful book written for publishing (computer) scientists.

Mostly discusses non-technical writing, but many of the points apply.

It is an extensive resource explaining how to write papers, reports, memoranda and Ph.D. thesis, how to make high-performance slides and oral presentations, how to avoid common pitfalls and mistakes in English, etc., with many examples of "good" and "bad" practices.

Generally useful advice that also applies to other networking conferences.