text:
Should considerations be made if half of a project group drops the course?
I am a student who is completing a course where the major assessment is a Project where you work in a team of four to complete it in different deadlines (Draft and Final).
However we have been informed that two of our group members have dropped the course and no considerations will be made. We are expected to work as a team of two when everyone else has a team of four (with some that I know of experience one person drop outs).
Would it not be ethical to make some sort of consideration? Maybe splitting my two person group and placing us in other groups that have had one member drop out?
The Lecturers reasoning is that "If someone is sick on your team you are still expected to make deadlines". However I know that if I had a deadline for a project and half of my team was off the entire time with some sickness then some sort of consideration would be made.
Is this ethical or am I looking at this wrong?
Thanks.
EDIT: The students are NOT COMING BACK
EDIT:
I just got back talking to him. He does not plan to change anything, stating that some people only have one person in their group now.
I said I would be happy to take them into our team but he said that was not an option. I emailed my unit coordinator as I believe this is unfair but I doubt anything will come of it.
At this stage we are just pushing on ahead but I am keeping my emails in case we fail and need to apply for a review.

Let's hope that your instructor will take this setback into account when grading your grouplet's project.  // Don't forget, sometimes a larger group is less effective than a really good partnership....

Is it fair? No, it's grossly unfair. But it can happen to you even in the job business. So, try to make the best out of it and you know what to write in the course evaluation. That being said, sometimes small groups do better work than larger ones, as aparente says. So, be your best, even if your lecturer clearly isn't.

I think it was worth asking once, to put into the lecturer's mind the idea that some adjustment might be appropriate. At this point, the time and effort you are putting into trying to get a change might be better spent doing the project. Meet with your remaining partner to determine what are the essentials for each deadline and concentrate on getting those done, just as you would in any hard deadline short-staffed project.

Men and months are equivalent only for perfectly partitionable tasks.  If you had to dig 12 miles of ditch in 12 weeks, losing half the team would be a disaster.  For a typical course project, unless both remaining team members are duds, your life has just gotten much easier.  (Thanks to Frederick Brooks for insight into partionability of tasks.  https://en.wikipedia.org/wiki/The_Mythical_Man-Month )

I've seen this happen before. What ended up happening was that Mr. Doe ended up being allowed to do an individual assignment on par with the level of effort as a member the group assignment.
Losing members does not change deadlines. The same result is expected, i.e. a paper is due on a deadline. That shouldn't change, even though your group has changed. Half a group doesn't mean you can get twice as long to work on it, mostly because deadlines rarely move as a result of group changes, and practically because the schedule won't account for it.
What has changed is the amount of work you can realistically accomplish. Instead of having four person-units to work with, you have two person-units to work with. You will not be able to make the same effort as four people, but I highly doubt the professor will expect you to be able to meet that level of effort, simply because asking a human to fill in as two humans rarely ever works in the real world, at least not for long before that human breaks down.
Do not expect to change your deadline. Expect to change how much work you will complete by that deadline. Talk with your professor about how much effort the two of you will need to apply to meet his expectations, as opposed to whether or not you will meet his deadlines. Does he expect you to perform as much work as four students will do, or just meet the same timeline?
If he also wants you to do twice as much work as other students, then I'd have issue, but if he only wants you to meet deadlines, and will take into account that you're short 50% of a normal group, then I wouldn't find any real merit to saying the deadline is unreasonable.

I just got back talking to him. He does not plan to change anything, stating that some people only have one person in their group now.
I said I would be happy to take them into our team but he said that was not an option. I emailed my unit coordinator as I believe this is unfair but I doubt anything will come of it.

Have you asked whether the marks will be adapted appropriately to take care of the circumstances which are not of your making?

His answer is that in a business your work wouldn't be lessened at all so the marks wont be adapted.

Well, I can't say you tried. It'll be tough work, but if he thinks you, or even a single student, can do it, maybe the assignment is geared toward a smaller than expected group size in the first place. Either abandon ship or tough it out.

The teacher does make a valid point in terms of work place environment though.  Say you are working on a project for company ACME.  Rough times have hit the company and now your team has went from 4 people to 2 people and the client is still expecting the project to be ready by the same date.  You wouldn't be able to go to the client and ask for an extension because it can be taken as a lack of ability.  If you tell the truth, the company recently let go 2 employees on the team, it shows the company may have some financial instability and the client may pull the project all together.
It sucks, I have experienced this in my previous job where I was on a development team of 10 and was chopped down to 4 while we were still expected to complete all the new projects while maintaining old projects all at the same time.
I have also experienced something similar with my senior college paper that we had to write as a group of 4 about 100 pages worth of information or about 25 pages each.  2 of our members decided the class was not worth their time and slacked off writing barely 10 pages each leaving much of the work to myself and another.  We approached the teacher and luckily for us, the grade was based on our individual parts you did and not as a whole group.
This though, is not as practical as my experience I first described to you because in the real-world, a client isn't going to go to an individual worker and say hey! this project was terrible, but your part was great!  They are going to go to your boss and say, this project is not what we agreed to, and not what we paid for.  FIX IT.  and you all get in trouble.
So while this may be a really lame situation, the teacher is actually preparing you for real-world situations that may come up.  Of course if you still feel strongly that you should be given consideration/moved to a new team, there is always the option of talking to the dean or department head to find out what they can do.  Usually though, they will stick to the teacher's choice.
With proper time management and understanding the priorities, you will find a way to get it done.  Don't try to tackle it all on at once, but break it down into segments.  if it is a 10 part project, talk it over with the remaining partner and sort them out.  Which parts are you stronger at, which parts are they stronger at.  See if you can balance out your weaknesses with their strengths and vice versa.  From there see what is left over to do and see of the ones that you guys have mutual weaknesses on, which one of you feels MORE Confident in taking on one of the parts.  From there just get working on it. You both know what you need to do, both know when they are due.  If you cannot move forward with a part until they get their segment done, work on a non critical path item that you can do on the side that is not waiting for a pre-req to be finished.
Edit: To the person saying comparison to the business is silly and can always rehire... this is not entirely true.  If a company let go people, the chances of them hiring anyone to take their place so you can get the project done is very unlikely.  Rehiring in general is a long process that can take months to fill in the gaps.  Are the workers just suppose to stop and wait for the new hire and blow all deadlines? No.  That doesn't happen.  Even if they start rehiring right away, you need at least a month or 2 just for interviews alone.  New hires may not be able to start right away due to still being at old job or the have a prior commitment and cannot start for a time period.  Working at jobs that isn't Mc'Donalds takes time to find suitable replacements with applicable experience.  Again assuming that there is no hiring freeze which there probably is one due to being let go.  not fired.  there is a difference between let go and fired.

While you are right that this sort of thing can certainly happen in the workplace, I don't think that necessarily means it's appropriate to replicate in an academic environment.

I just got back talking to him. He does not plan to change anything, stating that some people only have one person in their group now.
I said I would be happy to take them into our team but he said that was not an option. I emailed my unit coordinator as I believe this is unfair but I doubt anything will come of it.
At this stage we are just pushing on ahead but I am keeping my emails in case we fail and need to apply for a review.

@NateEldredge  I agree that it should not be applied in an academic environment for freshman and sophomores.  They are still trying to adjust to a new life.  However, by time you are a senior, you should be prepping for that real-world application.  This is why a lot of senior level classes involve internship in some form too.  Granted, I have always favored the stricter teachers and classes because I learned and grew the most during those classes. Anthony, sounds like this teacher could be playing God with grades if that many have group issues.  Definitely keep records of communication.

You have another option. Drop the class yourself.
The comparison with a business is silly as a business could hire new people to jump in and help out or they could reallocate resources. Your teacher cannot hire new students to take the class and seems to be unwilling to reallocate the teams.
Another way you could approach this is ask your teach to have a chat and explain to them your fear. That now a team of 2 has to do the work of 4 and that this could have a significant impact on the project as well as your final grade. Perhaps the teacher already has some thoughts on how she/he will mitigate these concerns.

This is my final unit in my course and it is past the census date. So if I dropped it, it would count as a fail.


text:
What do those evaluating fellowship candidates look for before accepting them to join their research group/department?
I'm applying for a new fellowship, and I'm wondering what senior lecturers and/or professors of a research group/department look into, for evaluating the fellowship candidates, before accepting them to join their research group/department?

Sadly, one of the more common reasons I have seen lately is that a lab wants to jump on the bandwagon of a current research trend (i.e., "Big Data" currently in CS) when the current members of the lab are actually not at all interested or knowledgeable about this new trend.

@xLeitix Yes, this is unfortunate at many levels.

@xLeitix I think it is legitimate for a professor to take someone that has expertise in a research direction they want to expand in.

I would say several points related to the candidates such as

How much in average do candidates publish per year?
Where do they publish?
What conferences they presented their papers in?
What are the impact factors for journals they publish in?
What new research questions or directions do they have?
How much citations did they received?
With whom in the group they can collaborate?
What funds do they have or can bring?
What experience in writing proposals do they have?
What are some similar proposals that received funds?
Where their research is going in the next 5-10 years?
What industry or academic experience do they have?
What skills do they have?
Can they enhance research group diversity?
Can they teach, present and discuss well?

I think this answers a completely different question (how do you evaluate a research group).

By they I meant candidates and not the group. So what advantages can the candidates bring to a research group, will be answer to at least some of these questions.

Good list. I think you could add: 'Does the candidate know a technique that could be useful to answer the issues encountered in the lab' (examples: a type of computational model or a specific microscopy technique, etc.)

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sort: votes


text:
Who Owns the Publishing Rights of my Ph.D. Thesis?
I wrote my Ph.D. thesis without copy/pasting any of my prior published articles. Now that the university library got hold of my Ph.D. thesis; I'm wondering who owns the entire publishing rights of my Ph.D. thesis?

Well, have you given them to someone else at some point?

@TobiasKildetoft What you mean? Ok, I'm updating the question with some background story.

Did you sign an agreement to transfer copyright to someone else?
Does your university policy (example) or employment contract specify that someone else holds the copyright to your thesis?
If the answer to both questions is "no" then you, as the author of the thesis, hold the copyright.
In the US, most university students retain the copyright for their thesis. Often they are required to grant the university and/or ProQuest a non-exclusive license to distribute the thesis, but without giving up copyright.

+1. I was not aware that some (evil) universities actually claim the copyright to the thesis. Thank you for the link. This is unheard of in the social sciences and humanities.

@RoboKaren I think things get more complicated when you start dealing with things like industry funding, patentable designs, or confidential data. These things might not directly reflect on the copyright, but you can start to see why places might want the option of more control.

As I commented on my answer, I think the MIT policy is an outlier.

You do, until you sign the rights away. This is regardless of whether you have a copyright page, thanks to the Berne Convention.
Note that depositing it in the library does not waive any of your rights.
You do transfer some rights when you deposit it through ProQuest but 1) you didn't mention doing so, 2) the form you would have signed if you had done so would have made what rights they wanted explicit, and 3) ProQuest does not request exclusive rights to publish and distribute your dissertation in any case , so you can publish parts or all of it in other venues without their permission.
Note that some publishers are hesitant to publish monographs based on dissertations accessible on ProQuest but this is a business decision not legal one. They fear the availability of the  ProQuest version is a market threat that draws away from the salability of a monograph based on the dissertation. As a result, some students have asked ProQuest to embargo the dissertation for a few years, which is something ProQuest is happy to do.

Note that although I wrote this answer, ff524's is the authoritative one because in some circumstances and in some universities, you do not own the copyright to your own dissertation.

A priori you own the rights. But you may have transferred them to the University (when signing a study agreement) or to a project (e.g. when you thesis is e.g. financed by an EU project) or to a company financing your thesis.
Most Universities do, nowadays, give the copyright to the PhD student.

I copyrighted my Ph.D. dissertation as soon as I was awarded by degree.  I am the only copyright owner. The dissertation was archived through a well known group in Michigan (Dissertation Databases
ProQuest Dissertations & Theses).  I did not sign over or transfer my copyright to them for their archives.  Anyone who wishes to use material from my dissertation must contact me for copyright permission before doing so.  Anyone who publishes work using any of my original data or ideas must also cite my dissertation in their bibliography to avoid copyright infringement or plagiarism claims.
I was disappointed to learn that my thesis advisor who was not a co-author of my dissertation decided to publish my work in a peer reviewed journal without my knowledge or permission.  He named himself author and did not cite my dissertation in the bibliography.  Neither the journal nor NYU cared to intervene on my behalf to correct and restitute me with authorship and citation as I was entitled.
The only recourse a student has against the theft of their dissertation in the absence of granting copyright permission to another person who wishes to publish work from the dissertation is to apply for and obtain the copyright first.  This does grant legal rights to the copyright owner in case of theft or wrong doing on the part of another person who appropriates material without permission.  Graduate students should insure a written agreement or statement from the university,their mentor or their Dept. guaranteeing them authorship rights to their original dissertation research prior to beginning a lengthy Ph.D. program.....otherwise they will become unpaid slaves for five to six years and end up with nothing to show for it in the end if these rights are not secured before they begin. A word to the wise but filing for copyright ownership is a form of security should they require legal protection down the road!

I don't think this is right.  If they pass your work off as their own, it's plagiarism regardless of the question of copyright infringement.  And in the US at least, my understanding is that copyright is implicit -- as soon as you write it, you are protected from someone else republishing it.  Neither the journal nor NYU cared to intervene on my behalf to correct and restitute me with authorship and citation as I was entitled.
Something seems fishy here.

There are hints of a real answer here, but they're hidden within a rant about your personal situation. I suggested editing your answer so that it sticks to the topic of the question.

@FredDouglis Copyright is implicit and automatic in any Berne Convention signatory, which the USA finally became in 1988, a hundred years after the initial signatories.

@DavidRicherby thanks for the confirmation. Took them long enough, eh?

@FredDouglis Yeah though, to be fair, the UK only fully implemented Berne in the same year, despite being one of the original signatories!

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PhD with Industry Research Group
For some context, I’m currently enrolled in a PhD program doing computational research. I’m also interning at a fairly large company in their research group as a research scientist over the summer and the area of research aligns closely with my interests. I would probably even venture to say that the areas I’m exploring in the internship are more interesting to me than the stuff being done in my current lab (or any lab) at school (although we’ll see if that’s true this summer).
I’m wondering if it’s ever possible to do a PhD thesis with a company and what that potentially would look like? For example, can I continue working on my projects from the internship over the academic year to contribute to my thesis? Can I get co-mentored by both my PI and internship manager (previous PI in academia and I think well-known in the field)? Are there any opportunities I haven’t even thought about yet that might be worth exploring? If anyone has any personal experience they’d be willing to share that’d be much appreciated! Just want to know what options I might have moving forward. Thank you!

This very much depends on the research culture of the company.  Does the company understand what a PhD degree entails?  For example, it may want you to work on an engineering project (or implementation of an existing solution), which is not publishable or/and does not help you complete your degree. Another issue is intellectual property.  This is usually the main hurdle.   In addition, some companies may have an extensive or lengthy review process before they release any papers for publications.

What is your country? PhDs as a collaboration contract between industry and academia are common in France and Germany, but the regulatory framework is very different. (I assume in the UK and US as well but I have no first-hand knowledge.)

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

Thank you for the feedback, appreciate the insights! On first pass the research with the company seems more exploratory but I'll have to see if that's actually the case. I am based in the US, and the company is fairly large (ie like FAIR/Google AI/Microsoft Research/etc), I'm not sure if that changes anything.

Better to look for the prior existence of such arrangements with other students who did the Ph.D. in the company. Personally, it all depends on your supervisor in uni and industry. Industries want to make a profit and they exploit technology and sometimes people who are developing it. So do professors in the university.
Note that in the industry your research will be constrained to what practical things can make money but you will be provided money and resources. In university, you can work on far-fetched ideas if you have a professor to support you.
If you like what is being done in the industry, take a sabbatical and continue that. It is important to do what you like and not what your university supervisor likes, or the industry manager. The advantage of working in the industry is you need not worry about the job after your studies or at least you have much brighter chances to land a satisfying job.
Best wishes!!

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Asking an advisor about the source of their funding
I am a PhD student in a program where everyone is funded by the department by default, generally through teach, but many students are sometimes funded their advisor. The upside to being funded by your advisor is basically that you don't have to teach, and funding after 5 years isn't guaranteed anymore.
I work in STEM and many people in my field get funding from the NSA, DOD, DHS, or other military groups. I personally would not feel comfortable accepting money that comes from such sources, due to my personal ethics. I am unsure of where my advisors funding comes from, this being the issue.
How do I politely bring up this issue with my advisor? It is important to me but I have a good relationship with my advisor and I don't want to seem ungrateful or like I'm criticizing them.

Has your advisor actually offered to fund you?

Yes, and has done so in the past, and will so do in the future when he has enough for all his students and it's "my turn to get a semester off".

That’s useful information, thanks. You may want to edit that into the question.

The agencies you listed very likely have publicly searchable databases of grants they’re funding. If you are really conflict-averse you can simply search there for your adviser’s name. But it seems simplest just to ask your advisor directly and mention your ethical qualms. That also creates an opportunity for dialogue, which might lead you (or him) to change how you look at the issue.

Those agencies don't generate money. The funding they distribute comes from taxpayers, like you.

First, you can just ask casually about his/her grants. There's nothing wrong about healthy curiosity. After all, you are pursuing a career in academia, part of which will be securing funding, so such an interest on your part is very natural. You don't even need to tell them where you are coming from, and if it turns out that the sources are kosher, that's it.
The second option would be just to talk upfront: you thought a lot about ethical issues and concluded that you are uncomfortable taking money from the military if it can be reasonably avoided. Focus on this as your resolution of a complex ethical dilemma, and convey that you understand that others could well resolve it differently.
A reasonable advisor should understand. It's a bit like you are vegan at a family dinner: your mom might think that you not eating spare ribs is ungrateful or criticizing, but then you have a larger problem than a family dinner :). Healty advisor - student relation assume that you trust each other enough to discuss such matters honestly and with respect.

+1 You don't necessarily need to start the conversation by talking about grants. If you're in a field where research has military implications, you could reasonably discuss the ethics of that with your supervisor. This might lead on to talking about money; even if it doesn't you will have laid some groundwork for asking questions if and when the supervisor offers you cash.

Really, asking about the source of funding means you do know a little about how academia works, and far from "nothing wrong with," it's a plus!

You can look at recent papers of your advisor in your general idea: usually the funding is acknowledged somewhere in the paper (often at the end).
Now you want to be careful about your interpretation of this source of funding.  Various agencies fund a lot of research that you may disagree with, but also a lot of research that you may agree with.  For instance, armed forces may fund research is prosthetics, mental health, and other areas clearly of value to them but also to society at large.
I would argue that the real question is if you are comfortable with your work rather than who funds your work. Would you be comfortable working on face recognition technology if it were funded by a private entity rather than some military or quasi-military agency?

I don’t think OP was asking for debates about what “the real question is”, or about whether their ethical beliefs are reasonable. The question being asked is pretty clear. (To be fair, I’ve been known to challenge the premises behind a question, so I see where you’re coming from, but just saying…)

@DanRomik the first part deals (indirectly) with the question.  I have challenged this premise myself (based on personal experiences).

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text:
When do new faculty find out about their teaching assignment?
I'm joining a US university as a tenure-track assistant professor in January. I was previously told that I will teach 1 undergraduate course in the spring semester which will start in the 2nd week of January. Despite sending multiple emails, I was unable to find out which course I would be teaching. The only answer I got so far is that "details about the teaching assignment will be sent out in due course".
I find this a bit concerning, as I think it will be very difficult to prepare an undergraduate course in just a few weeks that I have never taught before, while moving my family (with two small kids) across the continent during the December holiday season. However, maybe this is what I should expect. Thus my question:
How soon before the start of the term can one to expect to find out about the teaching assignment?

I doubt that your experience is typical. It is certainly sub-optimal.

I think a more interesting question would be "How much time should new faculty be given to prepare for their first classes?".

I don't suppose you could go into an online search portal and find yourself listed as teaching something?  It would be less official than your department telling you, but would give you something.  On the other hand, if they're not telling you, then it's likely that the online portal will just have a bunch of "To be determined".

These are not normal times but you should still normally know by now.  Even if it is not yet “official”, i.e. the Dean or someone higher up has not formally assigned you course(s), the Chair should have a very good idea of what you will be teaching.
Where I work the Chair submits recommendations for teaching loads to the Dean in March for the academic year starting in September, and the Dean officially informs instructors in late June/early July.
I don’t remember when we last had a January start, and lots of people are moving (albeit not at Xmas time), so you are not unique in that, but I have a friend starting in January at another school and she was given teaching duties months ago.

The actual answer to the question is, sadly, sometime between when it is possible and when it is convenient. But, I think that in your case it is approaching the time when it amounts to abuse (maybe too strong a word). It is possible that the administration doesn't understand the constraints of moving and such that are making the problem harder than it would be otherwise.
Most faculty (not newbies) usually know long in advance unless there are special circumstances. Some last minute changes do happen, since, for example, people leave or die. A scramble ensues. But usually the last minute changes are reasonable, as an experienced instructor can be found for that course, even someone who has taught it before in many cases. But an experienced instructor also has many tricks for ramping up a course quickly, especially if there are materials available from earlier instantiations.
It is much harder for a new, inexperienced, instructor, so they should be given longer than usual notice. For an experienced instructor, a couple of weeks is usually enough to "throw together" enough materials to get started and have an overall plan, even if some things still need development.
But, ideally, two things should happen, and they often do. First, a university does well to ease new faculty into the classroom with easier, and even lighter, assignments for a year (say). There is a lot for a tenure track professor to work in to in the first year or so. Also, ideally, such folks should be given a say in what they will teach, even if others need to yield favorite courses.
I'm assuming, of course, and hopefully not in vain, that people like department heads and deans really want the educational process to be done well and for new faculty to become productive in a mutually beneficial way. I worry that this is being neglected in your case.
However, as soon as you know what you are to teach (and accept it), try to get, from other faculty, any materials they have on the course, assuming it is regularly given. Consult with anyone available to get their sense of how to make it work. Don't neglect that the first few class sessions are important in setting the tone of a course as well as the expectations of the students.
Without all the other hassles (moving, etc) a month should be sufficient, but expect to have to lean on other people for help in this situation, both at home and at work.

It's not at all typical, but it happens, especially in the current environment of pandemic, mask mandates, mixed online classes, and uncertainty about enrollment.
Based on my experiences, the department probably has a pretty good idea of what course(s) they want you to teach. However the schedule is not set in stone until almost the first day of classes. E.g., there is a chance that some other course taught by someone more senior will be cancelled because of low enrollement, and that person might get "your" course instead, and you'll get "bumped" to some other course. On the totem pole, you're above the adjuncts, but below most full-time people. Or, e.g., it's possible that the course you're now penciled in for won't run, and you'll be given some oher course, perhaps "bumping" an adjunct.
The department probably has a pretty good idea of its second choice for your course might be. However they don't really know you yet, and, I'm guessing, perhaps based on their past bad experiences with other people, are afraid that if they give you too much information in writing, you will then complain (or even sue) if you end up teaching something other than what they originally planned. Perhaps next semester, after they get to know you better, they'll tell you more earlier. Or perhaps they don't tell anyone, in order to increase their flexibility to re-schedule at the last moment.

My experience is that in “normal” circumstances you know what you’re teaching well before the academic year starts, but that as soon as you’re in an “unusual” situation (eg your original class gets cancelled for under-enrollment, or your situation of getting hired mid-year) then it’s likely to get decided very last minute. The point is that you have to cover all the classes and you don’t want to re-assign someone who is already assigned a class, so you need to keep flexibility for those last few classes.

Much easier for an experienced instructor to "be flexible" than for a newbie.

For what it's worth, my department sent around teaching assignments for most everyone last week. Mine was for a course that I suspected would not make it because of low enrollment, and we pulled the plug on it on Friday. So I only learned my real assignment on Friday December 3.
I won't say that that's "typical", but what's typical anyway these days.


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Academic Papers - Language Translation and Writing Standards
I'm an undergraduate student, and I've been reading various academic papers for a Computer Security module I'm currently taking. Occasionally I run into papers with very strange/difficult to read sentence structure for example:

Vigenere cipher is a substitution technique
of polyalphabetic substitution. This cipher is
easy to understand and implement, therefore
the vigenere cipher is very well known.
Although (with the help of computers), many
computer security programs that use this
cipher [6]. The encryption process is carried
out by this cipher, by substituting plaintext
letters for the characters (letters or numbers)
of the key used and vice versa the decryption
process substitutes the ciphertext letters with
the same key (symmetric key). If the key
length is shorter than the length of the
plaintext, the key will be repeated, the
mathematical model of the vigenere cipher
encryption and decryption process is:

From https://arxiv.org/abs/1912.04519
Or this:

WEP try to use from five operations to decrypt the
received side (IV + Cipher text).At first, the Pre-Shared Key
and IV concatenated to make a secret key. Secondly, the
Cipher text and Secret Key go to in CR4 algorithm and a
plaintext come as a result. Thirdly, the ICV and plaintext will
separate. Fourthly, the plaintext goes to Integrity Algorithm
to make a new ICV (ICV’) and finally the new ICV (ICV‘)
compare with original ICV.

From https://ieeexplore.ieee.org/abstract/document/5234856
I feel like the intended meaning for both of these examples is seriously obscured by the writing style and I'm curious as to why they are written like this.
My obvious instinct is that this is some kind of language translation issue, since the papers are from departments in non-English speaking countries. I'm not sure though whether large journals might "bulk-translate" papers in a journal, or whether they require submissions in English in the first place and academics have to perform their own translation?
Does anyone have any insight as to how language translation issues are generally handled in academia, and why awkward writing styles like this make it through?
Many thanks.

Why do you think this is a translation issue, instead of this being an issue of not having English as primary language?

@Jeroen I'm not sure. It could be either - I don't really know how academia handles language barriers - why I'm asking. The first paper has its diagrams in a different language though, which makes me think there is some kind of translation happening somewhere along the line.

From my experience with peer review: the (english) language quality of some papers is  very low - most often the result of the authors not being native speakers.
While it is possible to address some of those issues during review, some problems will remain simply because peer review is not copy editing and if, after several rounds of review all important content-related issues have been resolved, the paper is most often not send in for another round just to fix spelling and grammar.
As for the translation suggestion: the vast majority of (english publishing) journals will require you to hand in your manuscript in english.


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Are P-hacking and hypothesising after results are known considered misconduct in academia?
This question is inspired by a comment to another question where I asked for help on how to argue against P-hacking and hypothesising after results are known (Harking). Someone questioned the classification of these two behaviours as misconduct, and my general experience (around my close academic circle) is that many see these two activities as part of the way we do science.
Here is what I refer to by P-hacking and Harking.
P-hacking is when someone collects more data, changes the specification of a statistical model, change the analysis sample, or does other changes to the study until the results become statistically significant. Many of these things can be done with a justification, but the p-hacker (p-fisher) does them solely with the intent of obtaining a significant result. In doing so, he or she risks capitalising on statistical error (type 1 error) and publishing results that are basically a false positive.
Harking is when someone generates a scientific hypothesis about the data after seeing the data. It would be innocuous if the researcher acknowledged the exploratory nature of the study and sought to confirm the findings in another set of data (or if he or she used cross validation techniques). It becomes a problem when researchers pretend that they had the hypothesis a priori and that the study was done to confirm it, hiding the exploratory nature of the study and conferring more strength to the results than they actually have.
I am not asking for opinions on whether these things should or should not be considered misconduct. Rather, I would like to know the overall position of scientists in fields where statistics are used. I know of no survey on how scientists view these two behaviours, but I welcome answers that include such data.

Harking is less standard, but is used by at least one person. P-hacking is quite standard and used in papers like this one.

The link for "one person" lists quite a few papers citing the linked paper. This suggests that "harking" is fairly widely used. (My own experience is that I have encountered the word several places, but that the word is not universally used, nor is the concept universally considered questionable.)

Never heard of harking before, but it sounds like a new term for data dredging.

Interesting (relevant) article here: British Journal of Pharmacology issues new experimental design standards

The Declaration of Helsinki was updated in 2013 to "mandate" that research involving human subjects must be pre-registered. While not perfect, pre-registration prevents many of these statistical manipulations. The idea of pre-registration is that publicly stating your hypotheses and the details of how they will be tested in advance reduces questionable statistical practices. For example, changing the number of subjects, the inclusion/exclusion criteria, or the statistical model are not allowed.
From my understanding, failure to comply with the Declaration of Helsinki would be considered unethical in Medicine, while in other fields the pre-registration aspect is being actively ignored. For example, articles are now being published with disclaimers like "this research was conducted in accordance with the 2013 Declaration of Helsinki except the study was not pre-registered".

Unfortunately, I have heard of instances where the "preregistration" was altered after the paper was published to accord with what was actually done, rather than what was pre-planned. (Sorry, I don't have a reference at hand.)

@Martha I ranted on [chat] about such a case a whole back. As I said, pre-registration is not perfect.

In the US, for research funded by the NIH, "Research Misconduct" is a finding made by the NIH Office of Research Integrity.  Other federal agencies have offices with similar responsibilities for research misconduct.
The ORI web site has a "RCR Casebook" of fictionalized example cases used in training researchers about responsible conduct of research and research misconduct.  It also has case summaries for every case where misconduct was determined and administrative penalties are currently in force (that is, cases where someone has been barred from getting NIH funding for some period of time.) In my reading through the training materials and case summaries, I haven't seen any cases where p-hacking was found to constitute misconduct.  The cases are much more about outright fabrication of data or suppression of inconvenient data (e.g. by throwing out "outliers")  to achieve a desired result.  It appears that from the ORI point of view p-hacking is not (yet) considered research misconduct.
More on what it takes to reach the level of "misconduct."  The NIH recognizes three kinds of research misconduct:

Fabrication: Making up data or results and recording or reporting
  them.
Falsification: Manipulating research materials, equipment, or
  processes, or changing or omitting data or results such that the
  research is not accurately represented in the research record.
Plagiarism: The appropriation of another person's ideas, processes,
  results, or words without giving appropriate credit.

p-hacking wouldn't fit under "fabrication" or "plagiarism."  It might count as "changing or omitting results such that the research is not accurately represented in the research record." However, the ORI also requires that:

There be a significant departure from accepted practices of the
  relevant research community; The misconduct be committed
  intentionally, knowingly, or recklessly; and The allegation be proven
  by a preponderance of the evidence.

That's a pretty high standard.  I think it would be hard to make the case that p-hacking is a significant departure from accepted practice and furthermore a researcher could claim that they didn't intentionally do the p-hacking.

I agree that it would be hard to make the case that p-hacking is a significant departure from (currently) accepted practice. However, if you have a thorough understanding of p-values, you will realize that p-hacking should be considered unacceptable practice. So standards need to change --  a hard task to accomplish.

As a practical matter it would be very difficult to prove that p-hacking had been done (and done intentionally.)  In terms of rule making the most effective strategy might be to require prespecification and registration along with some kind of review processor for all studies.  This is good practice and the stakes are high enough that it might very well happen in the world of clinical trials.  However, it would be very expensive (and impractical) to try to do this across the broader range of scientific research.

These types of data-straining behaviors are most certainly scientific sins, in the sense of being stains on one's conscience and reputation.  My favorite discussion of such sins is the "Nine Circles of Scientific Hell."
Building a formal misconduct case around such data-straining would likely be very difficult, however, since they may quite easily and naturally arise from human propensities to fool ourselves.  Many people who engage in de facto p-hacking are not aware of it, particularly when there are large volumes of data and powerful analytic tools in play.  A beautiful illustration of both the problem and an appropriate scientific response is the wonderful study that used popular fMRI methods to localize cognitive functions in the brain of a dead salmon.

There's a nice paper showing the prevalence of questionable research practices. For example, more than 65% of researchers who participated admitted to not reporting all dependent measures: https://www.cmu.edu/dietrich/sds/docs/loewenstein/MeasPrevalQuestTruthTelling.pdf
Unfortunately, they didn't ask about p-hacking with things like multiple analyses. I don't think running multiple specifications is a problem, however, and I think it's quite natural to do so.
To give a personal example: I once recoded a three-level variable in an experimental study into a binary response, as I was primarily interested in a treatment effect on one of the responses. This was transparently disclosed in the paper. However, a reviewer asked that I instead do the analysis with multinomial logit as that was more appropriate, even if it comes at a cost of making the coefficients more difficult to interpret (given the importance of communicating results, I think this is something that needs to be given some weight as well: the target audience would not be familiar with multinomial logit). So I redid the analysis as requested and dropped the logit regressions from the paper.
It turned out that the results were now stronger than before, so this adjustment worked in my favor. Suppose I had realized on my own that multinomial logit was more appropriate prior to submission. Would that have been p-hacking?
Consider another (fictitious) example. Suppose I have a regression with repeated observations from each participant and my treatment effect is significant if I use random effects at the individual level, but does not reach significance if I use fixed effects (or vice versa). In many analyses, either could easily be defended -- which one is "correct?" It can't just be the one I happened to choose first. It's a bit like using AIC or BIC for model selection: sometimes, one suggests the model provides a better complexity-adjusted fit, while the other suggests it doesn't. I don't think one is inherently better than the other.
The solution is to ask for more robustness checks in the analysis. Instead of showing only a model with an interaction, for example, the model without an interaction could be shown next to it. (This is actually quite common in empirical economics, where the goal is to show that the result holds under many possible specifications -- and not necessarily that one has found the one true specification.)
Pre-registering research makes sense in medical trials that are pretty straight forward: Group A gets Treatment X, Group B gets Treatment Y, and we compare outcomes on some dimension. If we compared the groups on 30 possible outcome measures, we'd likely see a difference in one of them. That obviously cannot be sufficient to establish efficacy.
But social science research is much more iterative and just doesn't work in the same way. Moreover, most recent papers report multiple studies that back up a particular claim. While the effect may still not be real, there are so many other things that are likely to be more problematic than the model specification. For example, there has recently been some work on incentives for creativity -- e.g. do people become more creative when you pay them more. Imagine the hundreds of different ways you could define and measure "creativity" and the hundreds of settings you could test this in (individuals? groups?). All of those are judgment calls and we won't know what generalizes until there are a dozen of these experiments (ideally by many different researchers).
Harking, it seems to me, is at least in part the result of how journal articles are written. They are not meant to be chronological accounts of one's thoughts, exploratory analyses, and eventual conclusions. They have to succinctly place work in a broader literature and convey the contributions of the present work. It may well be that prior to running the experiment, two theories would have been equally plausible -- but after running the study, only one of those is reaffirmed. If so, it seems like a natural stylistic choice to set up the study using the theories that are consistent with the results, then note in the discussion that the findings go against other possible explanations. (Including alternatives that the researcher may not have thought of, but that a reviewer connected the study to.)

Would you have switched to multinomial logit even if it would've made your results look weaker, or even insignificant? If yes, I'd say you're in the clear; if not, you might have been p-hacking. As for your other example, I'd say that, if there are indeed multiple reasonable hypotheses, the "right" thing to do would be to consider them all, but to adjust the significance level to correct for it.

A counter example is Grounded Theory.
It is a methodology used to build a theory out of data where you constantly try to match a hypothesis to your data. So your question about hypothesizing after the fact is definitely not unethical in this case!
It is worth noting that this is probably one of the few exceptions to the problems that the OP and other answers have been aimed at.


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Handling cases of "potential" ChatGPT-generated reviews in non-anonymous program committees (as a PC member)
I am currently serving on the program committee (PC) of a top-tier venue in computer science. Every paper is reviewed by 3-4 PC members; then, the authors can then see the reviews and respond with a "rebuttal." All the PC members who have reviewed a certain paper know the identities of the other PC members who reviewed the same paper. The reviewing process is double blind (authors do not know the identities of the reviewers, and reviewers do not know the identities of the authors).
The issue. I have just submitted my review for one of the papers in my stack. Upon reading the other reviews, I found one review is written in a "ChatGPT-like" style. Suffice it to say that it is not "obvious", but it's very likely that this is the case.
As a PC member -- but also as an author of a paper that was reviewed by ChatGPT -- I would like to "do something." However, I do not see anything that I can do that would lead to a happy ending. This is because I am unable to ethically prove1 that the review has been generated with ChatGPT.
As such, I am stuck. I have several options, but all of these do not lead to a happy ending:

I can contact the PC chairs. However, I cannot provide any proof, aside from mentioning the elements that make me believe that the review was generated with ChatGPT. Of course, lack of proof would also lead them to do nothing (e.g., they cannot "point the finger" to the reviewer without some solid basis).

I can ask the PC member who wrote the "suspicious" review. This is unlikely to end well, since they will most likely be defensive (if they truly used ChatGPT) or feel offended (if they did not).

I can acknowledge that I cannot prove anything, and try to induce the PC member who wrote such a review to amend the review by, e.g., pointing out specific elements in their review (which I think are vague and not informative). This may take a long time though, and may very well end with no change whatsoever.

I can just do nothing. And this would also leave a sour taste in my mouth: if the review was truly written by ChatGPT, then the authors would receive an "unfair" review, which would be quite disappointing.

If I knew the identity of the authors, I could ask for their permission to carry out the analysis myself. However, I cannot do so since the reviewing process is double blind.
Do you have any recommendation or alternative solution?

1: As I mentioned, I have been on the receiving end of a paper that (i) was rejected and (ii) for which one of the reviewers wrote their review with ChatGPT. After reading the reviews, I became suspicious and, since I was the author of the paper, I went to www.chatpdf.com, submitted the paper to it, and tried a few prompts until I found the one that produced (almost) the same review I received. However, I cannot do this "ethically" for this case: if I were to upload the paper on www.chatpdf.com, I would breach the confidentiality agreement since I would give a paper (currently under peer-review) to a third-party that is unrelated to the venue; potentially, I may compromise the novelty of the finding discussed in the paper, damaging the authors. Therefore, I am unable to prove that the review was generated with ChatGPT or similar LLM. [Somewhat related: Can I improve a paper peer-review using ChatGPT?]

It seems like you don't lose anything by at least talking with the Chair. They may do nothing, they may decide to contact the person who used Chat, or they may have some response you did not anticipate. What do you lose by contacting them?

@JoshuaZ talking with the chairs without clear evidence would (likely) annoy them, since they too cannot do much. The process of chairing a (top-tier) conference is extremely stressing nowadays, and I they have all the reasons to be annoyed if someone highlighted a "potential" problem for which they also could do little about.

I suspect you are overestimating both their likelihood of being annoyed and the level of annoyance.

This is a fairly long question for what could be written much shorter.

@AzorAhai-him- I have removed the first paragraph and put the explanation about "why I am unable to prove anything" at the bottom (since some may be aware of the reason).

The key is if the content of the review is poor or not. If there is a rebuttal phase, this can be elicited. If not, then it might make sense for the area chair to send the paper and the review for a meta-evaluation to get an idea if the reviewer actually talks about the paper in a coherent and grounded way (independently of whether they agree with the review).

But is the content of the review correct? If the reviewer looked at the paper and told chatgpt to write a review focusing on x, y, z, I don't see a problem. If the paper was so bad that even chatgpt could spot the problems, I don't see a problem.

Unless there's something really obvious like "as an AI language model", the problem is that what people consider  "ChatGPT-like style" is in reality mostly just "Nigerian english" because the models were fine-tuned with staff from nigeria https://businessday.ng/technology/article/online-uproar-over-nigerian-english-flagged-as-chatgpt-ish/
As a result, nigerian academics are sometimes getting wrongly accused of using AI based on style and terms more often used by nigerian english speakers.

For your third point, is it common for PC members to provide feedback on each other's reviews?

@DanielHatton That would be the original authors, who OP cannot contact directly due to blinded review (see the sentence in OP starting with "If I knew the identity of the authors"). Best to work with the chairs.

@Murphy I have reviewed your provided link and it does not support your claim that staff from Nigeria fine tuned the models.

@P.Shark: Maybe helpful: https://academia.stackexchange.com/q/199417/132071

@cong Chen  its that nigerian staff were hired for rlhf. They weren't doing the fine tuning themselves. https://www.theguardian.com/technology/2024/apr/16/techscape-ai-gadgest-humane-ai-pin-chatgpt

Many thanks @Murphy

I can contact the PC chairs. However, I cannot provide any proof, aside from mentioning the elements that make me believe that the review was generated with ChatGPT. Of course, lack of proof would also lead them to do nothing (e.g., they cannot "point the finger" to the reviewer without some solid basis).

I think this is the best approach. You can provide the elements that give you that belief, you can explain your inability to be certain, and then you can let them decide. I would not push it much further, though; once you've brought your suspicions to light your job is done.
As far as evidence, the chairs may have other evidence to bring to the table like other reviews by the same person that you are not party to.
As far as proof, this isn't a court of law, proof is not necessary; they do not need to pillory the accused in the town square, they could instead merely choose to discount their opinion in this one specific case. If sufficiently miffed, they could choose to not invite this reviewer to participate in the future. They can potentially do either of these things without making any accusation known, and they certainly don't need to prove involvement of any specific LLM, they must merely be dissatisfied with the quality of review.

At the very least,  you should report this to the chair because the authors most likely did not agree to have their abstract harvested by an LLM, and probably don't want it to be.

+1 for "Abstract harvesting" point.

By using certain services (e.g., chatpdf.com) it is possible to upload the whole manuscript and ask to write a review for it. This is what I described at the bottom of my question :)
To provide more context: the review does mention elements that are not discernible by the abstract.

@P.Shark Yes, but you didn't list it as a reason for a course of action :)

Is only the style reminiscent of ChatGPT or is the content of the review also poor? These are different questions. For instance, a reviewer not very proficient in English could have used an LLM to improve their writing, and that may give the style of ChatGPT but human content, or the reviewer could have discussed their technical points with a machine and taken on board some in their own judgement good points the machine made, or conversely humans are entirely capable of writing very poor/misguided reviews all on their own.
Depending on the confidentiality policy of your conference, it might be still ethical to try to reproduce the review on ChatGPT using their "temporary chat" feature (which wipes the discussion from the chat history, limits storage on OpenAI servers to one month, and excludes the content from training on user data). You could also try to see if a similar review can be reproduced by one of the more powerful open-weights models by installing a copy of, say, Llama-3-70b on a sufficiently powerful server that is fully under your control and feeding that model the text. It's easy to do, if you have the required server. But I think either method will still likely give you inconclusive data. It is easy with such things to see patterns where none are there (Google Toiletgate to get a taste for how low this can go).
I would simply strongly back up the authors if they contest any factually wrong claims made in the review, and/or use reviewer discussion unprompted to contest points that I think are weak/wrong, and try to convince the other reviewers/program chairs to discount the review if it is sufficiently poor on purely technical terms. Given such feedback, if the author of the review used ChatGPT, I would hope that they learn from the experience and produce better reviews next time they are asked.

I disagree on the "temporary chat" feature.  You're still sharing confidential information with a third party, without being in control if the data are truly wiped and excluded from training.

Yes, that's why I said "maybe ethical". There would definitively be an information security risk there. However, the number of trusted parties in a typical review process for a compsci conference is big: everyone on the PC can see all papers submitted unless they have a declared conflict, stuff is sent via unencrypted email, people do reviews on both their home and institutional machines, some of them use cloud backup, authentication to the review site is user+password, submissions are stored forever... the list goes on. Rationally I don't think the temporary chat would be that much worse.

@gerrit the same applies if someone emails a copy to themselves or saves something they're reviewing to their google drive.

@Murphy google drive, yes.  E-Mail, depends.

@Murphy FWIW, I have on at least one occasion received explicit instructions during peer review to not transmit the materials by email, to cloud storage, etc. I think these things will become more commonplace as more 3rd parties amend their terms to make it clear or plausible that they are using content to train AI models that people would have previously assumed was private or used in a far less intrusive manner. (Adobe's latest terms of service changes would be an example, Microsoft's "Recall").

@Polytropos even if the email communication is encrypted end to end, it may still break a badly written confidentiality agreement that is interpreted by its letter and not by its intent. Submitting encrypted data to a 3rd party is still submitting data to a 3rd party. That's why I agree with you that using the temporary chat is probably good enough, but if not, using a model on your own server has to be good enough. Overall I agree with you that the quality of the review is what matters, not how the peer came up with it.

Since the program committee has some control over who the reviewers are, the alternate solution is to have a policy about AI generated reviews and to get reviewers to agree to abide by those policies. This should, at least, reduce the problem if it exists.
The policy should be clear and give reasons for the rules.
One possible feature of such a policy could be that the reviewer attest on submitting the review that no part of it was AI generated (or something similar).
In the short term, however, the committee should be made aware of any suspected AI generated reviews so that they can evaluate the problem.
Authors need input on their submissions and the input needs to be done by a mind with an ethical sense behind it. If it could be automated then there would be no need for reviewers. Reviewers need clear rules about many things including AI generation. Without that combination, the quality of presented and published work can and probably will decline.
And, this isn't limited to CS.

I would ignore whether the review was written with the assistance of a LLM. It isn't really important and it is hard to prove (And is there a point to proving this? Is there an explicit policy against this?) What you really have a problem with is that the review is of low quality. The same problem can easily occur without LLM assitance. Is there a procedure for when you notice a review of low quality? If so follow that. In case you can still edit your review perhaps you can discuss the points mentioned in the other review and rebut them? It also seems like you will be in the position to point out that this quality is low quality and should be ignored later on in the process, or are you not? If not you should write someone who is.

You should do absolutely nothing.
The premise makes an implication that is not shown to be true. More specifically that there's something wrong with using tools to write reviews.
Is that explicitly forbidden (although it shouldn't be in my opinion)? If yes, address it to whoever issued the policy. If it's not explicitly forbidden, then it's allowed.
The reviewer is nevertheless responsible for the reviews they sign. If they used ChatGPT and then agreed with what it says there, then it's all good. If what it says doesn't make sense, then they're responsible for what they signed nevertheless.
If they're plagiarizing, or if it's a low quality review, address that, but not that they used a glorified spell checker to improve their writing efficiency.
In the comments to this question it has been raised the issue of possibly breaking a confidentiality agreement, as some GPT tools may not be associated with the program and may involve submitting the data to a 3rd party. This doesn't necessarily seem to be the raised issue, but I find this to be a legitimate concern. In this case, the issue should be addressed directly and no proof is necessary.
It should be as simple as "Hey, I noticed a certain style of writing that made me think that you may have used a tool that may require submitting the article to a 3rd party, which may be in breach of our confidentiality agreement. Please double check that this isn't the case, and if it is, please let us know so that we can take the further appropriate steps."

Comments have been moved to chat; please do not continue the discussion here. Before posting a comment below this one, please review the purposes of comments. Comments that do not request clarification or suggest improvements usually belong as an answer, on [meta], or in [chat]. Comments continuing discussion may be removed.

"A glorified spelling checker" - false equivalence - a spelling checker is supposed to correct what the author wrote following their path not springing off into unknown directions to harvest as much of the author's intellect as possible - and we know how twisted these simple spell checkers are with the ambiguity of straight forward English words let alone longer passages.


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Can I ask a DST INSPIRE faculty for a recommendation letter?
I will be applying for PhD in USA this year, so I will be needing recommendation letters. Can I ask a DST INSPIRE faculty who used to work in same lab as me (apart from my principal investigator)? Will US universities consider this reference because I don't know if they are aware of what is a DST INSPIRE faculty. Will it pose a problem because DST INSPIRE faculty have a tenure of 5 years and I don't know if will they become permanent after that. Also, they are not given the designation of an "assistant professor" and are call "DST INSPIRE faculty" so I had this question?
DST INSPIRE faculty: It is a scheme by the Department of Science and Technology (DST) to offer an opportunity to young talents in India through contractual and tenure track positions for 5 years in both basic and applied sciences area. And INSPIRE means Innovation in Science Pursuit for Inspired Research.

Since neither I no @scaaahu know what a DST INSPIRE faculty is, it is at the very least necessary that the letter explains who the letter writer is and what DST INSPIRE is.

What made you think a DST INSPIRE faculty member with 5 year tenure cannot write recommendation letter?

The universities might not know what is an INSPIRE faculty. Also, they are not given the designation of an "assistant professor" and are call "DST INSPIRE faculty" so I had this question?

Please edit the question with the info you include in the comment above to make the question clearer.

"I don't know if they are aware of what is a DST INSPIRE faculty" make your life and everybod else life easy: assume they are not.
And no one cares, because national grant in counry X are not relevant to people in country Y. You can however concisely explain that your recommender is holder of an highly competitive national grant (or whatever it is).

In general, any mentor with good scientific or academic qualifications can be a good recommender.  It's always better to have somebody who can speak strongly about you, even if they're in a "non-standard" academic position.
That said, it's important that your letter-writer make it clear who they are and why their opinion should be taken seriously, especially when writing to an international recipient who may not know their system.  That might include explaining what a "DST INSPIRE" faculty position is, or it might just mean pointing at the letter-writer's own CV of scientific accomplishments.

National grant in country X are, in general, not relevant to people in country Y. Please assume that when writing to people in country Y, you have to clearly and concisely explain what are your country conventions&the likes. If they are too complex, it is probably worthless to explain them anyhow.
In your cover letter, briefly introduce the recommendation letter writer, mentioning any relevant grant and title he has in a way that is relatable to the receipent background.
Which means:
no) "DST INSPIRE faculty"
not yet good, but better) fixed-term faculty appointeed under the highly competitive DST INSPIRE national program (acceptance rate 1~10%)


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Why do some admission applications ask what you've been doing in the years since you left school?
Some admission applications ask what you've been doing in the years since you last left school.  For example, work, military, parenting, volunteering, or caring for a sick family member.
Some applicants might not have been doing much.  Perhaps they spent a few years on video gaming, or on Reddit, or lying in bed with depression.
They might have decided to better their life by going back to school.  Having to face such a question might only discourage them from applying.
In addition, if the question is mandatory, one could argue that it discriminates against those applicants who've spent years lying in bed with depression.  In fact, as soon as the person becomes busy with school, they now have something to do.  Therefore, their depression might possibly go into remission.  It might not return at all before they graduate.
Why do such questions exist?

I'm sorry, questions about undergraduate admissions are off-topic.

I appreciate your feedback!  I've edited the question so that it hopefully also covers law, medical, and other graduate admissions.  Could you please retract your close vote, if any?

I guess I'm wondering why you think grad school admissions wouldn't want to know what you've been doing since you graduated college ...

@AzorAhai-him-:  To avoid making the applicant feel bad — in case they've spent multiple years since college doing nothing productive at all.

I really don’t think admissions committees need to worry about making applicants “feel bad” by asking them what they’ve done with their life over the past two years. What, do you want them to just let everyone in who wants to?

@AzorAhai-him-:  Fair points.

Admissions is meant to gauge how well you fit a program and whether you'll be able to be able to successfully complete the degree.
Someone who is successfully doing other things is more likely to be able to complete a degree than someone who is not. Some decision needs to be made, not everyone will be admitted. Not everyone with depression is lying in bed or playing video games with all their time, I think that is a caricature of what the reality of depression is. If someone is so severely depressed that they have been unable to do anything for years, I do not think they are a good fit for graduate school at the moment, they should focus on their health first. I am not aware of any medical backing for the idea that enrolling in a degree program is a cure for depression.

School is definitely not a guaranteed cure for depression.  Still, if the person really isn't doing much with their time, it might help somewhat.  See, for example, the chapter of Feeling Good, by David Burns, MD, about "do-nothingism".


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Replying the rejection email from Professor
Recently I mailed a professor for a PhD position and got a reply after two days.
The following is the reply I received from the prof.

I am afraid we already found a candidate to fill this position.
      There will be other opportunities in the future in our group.
      We are truly sorry for not bringing more positive news, and I would like
      wish you the best on your job search.

A thank you and good luck reply is needed?

There is no reply needed from you to them.
Move on to your other applications.

+1. Professors get hundreds of emails each day, no need for small talk.