text:
Is passion a must-have for a Master's student?
When I was accepted to become a Master's student, I had virtually no experience in the topics that my supervisor wanted to work in. Lately I've been catching up on background knowledge (I've been in the program for about a month), but I still haven't developed that ever-elusive passion that I probably should have by now.
So why did I choose to work with this supervisor? I chose a supervisor based on personality, and I decided to take a risk with a brand new topic because I was feeling jaded about what I had studied in undergrad.
I'm not asking "how do I find my subject?". I want to know if this lack of an all-consuming obsession with a particular subject is a real cause for concern. What should I do, and what do you suppose my supervisor would want me to do?

Scholarly passion is a myth more than a reality. Compare your research to a love relationship. Perhaps love at first sight does exist, but the most stable and deep relationships are not necessarily those that start as a passionate affair, notwithstanding popular belief. It takes time.

Not any more than it is required for high school.

Sometimes the dedicated but passionless are better at almost anything because they are more consistent while a passion-driven person may only be motivated correctly when they are interested in something.

Yeah. I am very passionate about the subject I'm doing my Master's in. The result is that I'm spending years and years on it... I think this is a good thing (fortunately my university supports it), but most people would probably say it's better to get on with a PhD and/or other things in life. Which is perhaps easier if you're not too passionate...

Don’t confuse passion for commitment.

For anything that seems to require passion, you can often substitute strong work ethic, or some combination of work ethic and latent skill.

"passion definition: 1. a very powerful feeling, for example of sexual attraction, love, hate, anger, or other emotion" ... I think you mean enthusiasm for the subject.

Passion is not necessary for a master’s degree.
You’re still at the stage of your career where you’re getting your feet wet with research. The important thing right now is to separate your feelings about research in general from your feelings about the specific research topic you’re exploring. You can always move to another topic for your PhD, and nowadays, you’ll almost certainly have to change focus areas multiple times in your career.
At this point in your career, being able to determine that:

You love research and your focus area;
You love research but not your focus area;
You love your focus area, but not doing research in it; or even
You don’t love research

are all perfectly valid outcomes because they will help you to decide on a long-term path.

I think I should write these 4 points in my greenboard. Just for curiosity, does these apply post PhD??

It might be hard to ascertain if you love research but not your focus area, though...one might cloud the other...but of course that doesn't prevent one from slogging through and getting a Masters' :)

That's why I said it's important to separate the two when judging how the project went. My first project convinced me research was the right career for me—but that the research area very much was not.

@Coder Yes. Post-PhD it's in a way easier to decide that you lost passion for your field, because you are much more flexible in adapting your field of study without having to worry how the new research will fit into your dissertation.

@xLeitix Absolutely right what you said.

I teach on an MSc programme and I would say:
Passion is absolutely necessary for an "academic" Masters, i.e. getting a distinction, publishing a paper, building a serious research relationship with a supervisor, then using all of the above to go on to PhD and maybe academic career. Especially for a masters by research rather than taught. If you are not passionate about your subject then you should not be pursuing such careers in the first place as they will make you utterly miserable for your whole life.  (Real passion here means: you love this stuff so much that you are willing to devote the next 10-20 years of your life constantly relocating to random countries where the work is, getting paid nothing, and working on insecure 1-3 year contracts, probably away from your partner and family for much of it, who are trying to pay a mortgage somewhere else, all for about a 1 in 10 chance of getting a permanent academic post somewhere, also in a random country. Not recommended for most people at all.)
Passion is not necessary for a "professional" masters.  The vast bulk of masters students are in this category and use it as training to get a better, higher-paid (non academic) job, and are often successful in doing so.  In some professions (eg. chartered engineering) a masters is a necessary box to tick as part of gaining professional status. You probably still won't do very well if you really don't like your subject, but "passion" is an overused and unnecessary word for "yeah this subject's OK and its a nice/stable/well-paid career" which is a great goal to pursue for most students.

Passion helps, but this is a precious resource and it can (and often does) run out, particularly on the course of several years required for MSc or PhD program.
To a certain extent, passion (generally called motivation) is normally expected of candidates in academia, so even if you do not have it, you have to show it at some point in your motivation letter and/or in your interview.
In addition to other brilliant answers, please note that MSc/PhD program is a two-side investment: student invests time and often money, but also the supervisers invest time, passion and sometimes are expected go above and beyond their contractual obligations to help students succeed. This is a rewarding process if (and only if) both parties are engaged and the result proves efforts worthy.

In order to avoid getting the wrong kind of answers on this post, I deliberately did not include my motivations for asking this question. Quite simply, I was becoming concerned about meeting my supervisor's expectations, and so I did a bit of research. Many sources said passion is important, but I couldn't agree more with your statement that it can "run out"

I disagree.  Motivation is not the same thing as passion.  Passion might be one motivation, but IMHO it's far from the best, as it's likely to lead one into irrationality.  (See e.g. "Romeo and Juliet", among countless other cautionary tales :-))  A far better motivation is the desire to earn a decent living doing work that is interesting, and which you can do well.

@jamesqf When PI wants a passionate pamphlet from a candidate, he asks to submit a letter of motivation with their CV. I am not saying passion and motivation are the same thing, but in academia they say "motivation" when they expect passion.

@Dmitry Savostyanov: What the heck is a "passionate pamphlet", and why would a PI want one?

I would second the idea that passion can "run out". Especially when you work on one problem which turns into a protracted series of dead ends and which raises more questions than answers. At this point you realize that it takes a lot more than interest or passion to find the motivation to push through and keep going. For example, focusing on the fact that you have a job to do, and keeping an eye on the horizon for the possibilities of interesting results.

@jamesqf Basically, some PIs seriously expect everyone in their lab to be super-passionate about the subject, and happy to work there no matter what. I am happy for you if you never met such PIs in you career.

@Dmitry Savostyanov: I'm happy about that, too.  Though perhaps it's a consequence of being a CS person working in non-CS labs, so rather than being deeply involved in the research, I just had to understand enough of the science to turn it into efficient computer models.

Passion certainly helps. When you have passion for what you are doing, it feels much less like work and more like self-fulfillment. But when you lack passion for what you need to be doing to advance your life planning, you can substitute it with:

Having rigorous self discipline and clinging to the hope that one day the drudge will be over.
Alotting a bit of your time for something else which provides you with self-fulfillment. Pursuing an art or craft, doing sports, a low-intensity personal research project or spending time with people you hold dear can provide you with the mental energy necessary to withstand the boredom of doing the stuff which needs to be done without losing your sanity.

I know people who ended up detesting the subject of their master thesis, but they pulled through because they knew that they had to succeed in order to progress with their life plan. Many of them did eventually succeeded.

I want to know if this lack of an all-consuming obsession with a particular subject is a real cause for concern.

Yes, but not reason to give up your Masters' program. What I suggest is:

Try to maneuver the rest of your Masters' in directions which you feel might stir your passions, or just such that are significantly different than what you've done so far. Not at the expense of failing to pursue your approved subject, if you have one - but to sort of probe around for something to get passionately into later on.
Don't dare start a PhD without feeling very enthused and passionate about digging into a specific subject - certainly not when it's the subject you muddled through an uninspiring Master's about.

"Don't dare start a PhD without feeling very enthused and passionate about digging into a specific subject": This advice does not apply if you know you want to do research but your program doesn't require you to choose a project right away.

@aeismail: In my opinion - it does apply.

All I knew when I started grad school was that I really liked doing research, but couldn't tell you exactly what area I wanted to study for the next few years—just that I wasn't going to be an experimentalist. By your logic, I shouldn't have started my PhD. (Then again, I only had a bachelor's, not a master's.)

I was told this by several professors when I was entering grad school. I was going to grad school to find if I had that passion, not because I already had it. I hated the way some academics tried to scare me out of grad school even as I was doing well there, because I treated it like an interesting and rewarding job to try, but not like my life's calling. Just tell people "if you want to try grad school, don't let strangers tell you that you shouldn't". Even trying it and finding out you dislike it can be a valuable experience -- you'll never look back and wonder "if only"!

@Sam: 1. Maybe it's different in different academic disciplines. 2. In the departments I'm familiar with - there's no "trying" and it's not a school. It's a job as a junior researcher. You can always take grad school courses (often already as an undergrad) or read their textbooks, of course.

@einpoklum 1. Yes, probably. 2. In the U.S. (physics perspective), many new grad students come from schools where it wasn't possible to take grad-level courses or do important research. I.e., we have to apply and try it out to know at all whether we'd enjoy it. It isn't uncommon for some students to leave after their first year because they've learned they don't like it. Still, some professors act like the incoming 22-year-olds need to know that it will become their life's work to decide to get a post-grad degree.

@Sam: For a doctorate - maybe not your life's work, but something that very important to you. If it isn't, well, maybe you should fill out a lottery card (don't comment till you've read the link).

I think it really depends on individuals. I did my Master degree in the U.K., which usually lasts 12 months.
I think most people could survive a year without "passion" for the sake of just doing a degree. But it is less likely to do a PhD without passion, which last about 3 to 4 years in the U.K. typically.
Just my honest opinion.

No
Passion is not necessary for a masters degree. Merely intelligence and resilience.

Welcome on the Academia SE! I suggest to write more reasoning to your answers.

Yes you should be passionate! You are paying a big price to be there! A masters degree is a lifetime investment. Many years will be spent completing your degree, a lack of passion will result in a lack of satisfaction upon completion.

Usually you don't need many years for a master's degree, just 2 years.

Actually, some over-passionate students may also feel dissatisfaction upon completion, particularly if their passion is due to wrong expectations.

This is the right answer. It seems like everyone on this page is dead inside and can't remember being passionate for their field, or somehow thinks that doesn't matter, that a grad student should just be a lab rat or research slave. Passion for a particular research project goes up and down, but passion for a field and passion for research in general are critical. If you don't have that, you are only wasting years of your precious life.

You did not mention a country, but at least in Europe the Master's degree is the marker of the end of your studies. And usually nothing more.
If you finish your studies without a master (but having passed all the exams), they are essentially lost - you are technically "something" (what depends on the country), but not the normal guy who ended correctly his studies.
This is to say that the Master is usually the end of scientific studies for people in Europe and a compulsory purgatory before going on.
The quality of the average master thesis is low to say the least because those who were writing it had other more important things on their minds (looking for a job, party hard like it was the end of youth, etc.).
So do not worry if you do not have the passion. It is more worrisome if you plan to continue in science - not because of the subject (you can change that) but rather the mindset.
Again, this may depend on the country.

I think European people don't usually do anything after their undergraduate...very few do a Master.

@ChenStatsYu in average 78% of studenrs which get a license (3 years of studies) continue with a master (87% in law). This is for France, the rest of Europe is similar. Before the Bologna reform which brought in a 3+2 years cursus more or less homogenous in Europr, it was even more. So no, the vast majority go for a master degree as it is way superior in the market than a license in Europe.

haha maybe just in the UK then. Lots of pupils just "go straight" into work after undergraduate degree. Tuition fee is another issue here.


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What is a proper balance between reading research papers and reading books?
I am a student of electrical engineering and am currently involved in research. In order to remain active in research, I think we have to constantly keep reading the latest research papers.
However reading books is also very important. What is a good trade-off between reading books and papers? How many research papers should students be reading on a weekly basis? I will be thankful for any suggestions.

If you're talking about as a grad student, books are a very big time investment you'd probably be better off avoiding.

@ASimpleAlgorithm That depends on what sort of books. I took this to mean textbooks at various levels.

@TobiasKildetoft Indeed that's what I took it to mean as well. Note I said "probably", as in you aren't always better off avoiding them. But, given the typical grad student's propensity for trying to become an expert in everything that seems like a promising alternative approach, versus actually doing the hard work, I'd advise avoiding them...

Reading few pages of a book a day can be manageable, especially if you aim at specific chapters!

I make a simple distinction based on what I'm looking for. Essentially, am I tackling a problem that I know about, or am I looking for a new problem/area to expand into?
I'd suggest you to have atleast this broad an idea before dipping into any material, otherwise you may easily get lost in the sea of information available today. The risk of getting too deep into something and forgetting why you got there on the first place is very real.

EDIT: For this problem, I'd recommend taking a look at this question.

Now, if a problem is known, I'd go straight to research papers and get an idea of what approach people follow, what is being tried, what gap areas exist, and so on. I'd go to a book only if (a) it is referenced in a part of the paper I find relevant, or (b) the explanation proposed hinges on something I know very little of. In (b), a book is good because typically it is written in a more pedagogical style and hence easier to understand.
On the other hand, if I'm looking for an area to expand into, I'd first consult books on related topics to acquire some understanding before jumping in to literature. Yes, it is time consuming, and reading papers directly seems like a shortcut ('you anyway need to read them once you select your problem, so why not start there'). Nevertheless, I personally believe that a firm understanding at the beginning is necessary to attempt formulating your problem.
Just to reinforce, let us consider these two situations and see what happens if we reverse the approaches.
(a) You know the problem already, but start off with a textbook- you will spend a lot of time reading. Since a good book is engaging, you will find it hard to put down, and may start jumping from the chapter you were interested in to another chapter that is referenced somewhere (another quality of good books). At the end, you've spent a lot of time, gathered a lot of information and hopefully some knowledge, but you are no further on your research problem.
(b) You don't know a field, but start reading literature- you may get lost in jargon. Also, you may think a contextual statement is generally valid, leading to misconceptions. Finally, you will rely completely on the approach and methodology of a particular author with a particular agenda (i.e. to advance a particular viewpoint/theory) and therefore adopt all his/her biases. A book will generally be written in a neutral way (the agenda is to disseminate information), which will help you to critically analyse any research result.

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In Chinese academia, what is the significance of the corresponding author, beyond 'public relation'?
I am a PhD candidate currently preparing a manuscript for journal submission in the environmental modelling and engineering field. One of my authors is from China, and contributed some data and ideas for the methodology that I found quite useful.
This person wants to be the corresponding author for the study with the argument that being the corresponding author is the only way to secure future funding for the study in China/from Chinese sources. Yet he is no way in a position to explain and defend the study, as he wasn't really involved in modelling, analysis and writing, and has limited understanding regarding the main model functioning.
I have so far refused to let him be the corresponding author, but he is really putting pressure on me to be the corresponding author.
Can someone give me some pointers on why it is so important for a Chinese academic to be the corresponding author? Does his argument about funding generally hold true?

Even if he's right that making him the corresponding author would be of great benefit in securing Chinese funding, that's not an appropriate basis for the decision.  (So I hope you are just asking in an attempt to understand his position.)

Yes, I want to understand why it seems so important to him.

Are you sure that you mean "corresponding author" and not "first author"?  The literal meaning of the former is simply "the author who corresponds with the journal" and it is not a position of prestige per se.  It is also not necessarily divulged in the published paper who is the corresponding author but is more a matter of internal "workflow".  At least that is my experience (my field is mathematics): moreover, in my experience the corresponding author is usually the most senior author, but not necessarily.

@PeteL.Clark I am pretty sure it is the "corresponding authorship" the Chinese academic wants. I have some clues about the reason why. I don't want to give an answer I am not sure of. So, I am waiting for somebody who has more knowledge to answer.

related: http://academia.stackexchange.com/questions/10062/does-corresponding-author-carry-an-implied-meaning

@scaaahu: Interesting.  I hope the information eventually comes out.

@Pete: The answer cited by Federico shows that in some fields/cultures, being corresponding author does carry additional prestige.

@scaaahu: please go ahead and share your thoughts/clues/insights...

@Nate: In certain fields, and in a perhaps somewhat nebulous way (the most extreme answer says that in economics, some people list corresponding author status on their CVs), yes.  I think this is part of the answer, but it sounds like it is not all of it.  In particular I don't understand why someone would lobby for corresponding authorship but not first authorship (which I think exists in the OP's field).

From Li (2013): "However, such practice of the student who has done the benchwork and written an early draft being listed as the first author and the supervisor who has played the dominant role in writing and revising the paper as the corresponding author seems common in China.". As such, my co-author would appear as more 'important' in Chinese academic circles as me, the first author.

@G-wizard: That sounds like a reasonable explanation.  (As others have pointed out: just because someone has external reasons to want to be a corresponding author does necessarily make it appropriate for them to be so.)

@PeteL.Clark: "moreover, in my experience the corresponding author is usually the most senior author" - that is interesting; in CS, I am used to situations that the corresponding author is usually the least senior author (the more senior, the more e-mails the person gets anyway, so no professor I know would agree to spend any time on organisational micromanagement such as signing copyright transfer agreements). If something important related to the contents comes up, the corresponding author can of course still internally contact the other authors.

@O.R. Mapper: Well, in mathematics (things are changing but) more than half the time there is still only one author.  So there is literally no choice...

I have been doing research in China for 10 years now, and hopefully I can answer your question. When a candidate is being considered for a faculty job here, only those papers where he/she was a first author or corresponding author are counted. This is because there is a rampant abuse of honorary authorships, namely colleagues and students are added even though their contribution was minimal or none at all. As usual, instead of cracking down on the abusers, more rules and regulations are put in place. This is why in most papers published from China, there are around three to four first co-authors (whom contributed equally) along with four to six corresponding authors. The consensus is currently being upped another level where some institutions are not counting corresponding authors.

If he's just one of the many authors, this paper your are talking about is of 'no use' to him. He can't use it to claim rewards of any kinds (funding, awards etc)  in China. However if he is the 'corresponding author' (or first author), he is able to claim that he played an important part in the paper and this IS his paper...He's not lying in saying "...is the only way to secure future funding for the study in China/from Chinese sources..." but do you want to let him do that?

I did hear this explanation before. I wasn't totally convinced, though. This was a reason to claim 'corresponding author' three decades ago. But, now many Chinese understand the difference between 'corresponding author' and first author/author with most contribution. I am not sure this trick is still useful. I gave an upvote because it is consistent with what I heard of before (I am a native Chinese speaker although I am not an insider).

Note that corresponding author is not the same as first author, as this answer seems to imply.

@MarcClaesen The OP of this answer did differentiate the two by "(or first author)". Notice the "or" within the paren. I believe he meant either corresponding author or first author would do the trick. I heard of this more than once before long time ago when most Chinese scholars did not understand what they mean. All they knew was that you were an important author because your name was singled out (corresponding author).

I am currently a foreigner postdoc in Mainland China, and have been here since almost 2 years. I have discussed this specific matter with colleagues, and also often run in the same moral conflict. Therefore I hereby write from personal experience.
The issue the OP puts forward boils down as, why should somebody who is unable answer any specific technical questions about a published study figure as a corresponding author?
The practice of including supervisors as corresponding authors is widespread in China for a number of reasons. Still nowadays for many funding opportunities (and refunding & rewarding as well, concerning publication charges and prizes) the published status as corresponding author is a requirement. Moreover the corresponding author is regarded as higher in the hierarchy in the local strong leader & face culture. This is why this person is pushing to be listed as corresponding author, and also because pushing for short-term goals is also common practice in Mainland China.
I have until now resisted against making "honorary" corresponding authors, but this frequently leads to a conflict. Not only in China, but here this is taken more seriously and may lead continued issues.

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Applying for another PhD after dropping out 4 years ago, should I use my previous PhD supervisor or undergraduate supervisor for a reference?
I dropped out of my previous PhD 4 years ago, because my UK institution had a four year maximum time limit for completion which I didn't meet due to my mental health issues. I'm now recovered and have been working for the last three years in a research position at another university. I would now like to apply to do my PhD part-time in the department where I work.
I think my previous supervisor would give me a reasonable reference as he was sympathetic to my issues, but I am concerned about how it would look to use an academic reference from a qualification I didn't complete. My only alternative would be a generic reference from my undergraduate study director who will not even remember me as I only met him a handful of times and I completed that degree ten years ago.
Which would look best?

can you not ask your supervisor from your research position at another university? (the one you've been working at for the last 3 years?)

@PandaPants Presumably OP means in addition to that person, since it is standard to provide >1 reference (usually 2-4 in my experience).

@BryanKrause ah I see, thanks. that makes sense. silly me.

I wonder if it is worth flagging up that the previous institution behaved incorrectly here. The 4 year limit is due to UKRI monitoring the 4 year completion rate in order to decide funding rates for universities. However, they make an exception for health related delays, and the will give the university an extension this case, which they should pass on to the student. It would be bad to give the impression to student struggling with mental health issues that they can't take a leave of absence as then they might not finish in time.

Assuming your resume includes the fact that you spent 4 years doing PhD research and were not awarded the degree, I don't see the problem with asking your former supervisor for a reference. I assume this is the case, because you still need to account for the 4 years in your work history. In that case, your supervisor's letter could be valuable in explaining that you weren't told to leave the program due to other concerning issues; this was for health reasons.
But here's the key: can your former supervisor write a strong letter of recommendation? Ask him or her explicitly whether they can do that. If not, move on to a different reference.
Think about any other references you might be able to call upon in your current position as an alternative. Do they need to be professors? Have you worked with research associates or other professionals who can vouch for you?


text:
I am going to start my PhD in the next few months. How you would you recommend that I start networking?
I am an Indian and I will be starting as a Chemical Engineering Ph.D. student in the US this fall semester and I am looking for some tips regarding how I should start networking before I start my graduate studies.

Field? I know somethings that are relevent to some fields and not otherd

Chemical Engineering

I don't think you can actually start networking until you are a student, but you can find out what kind of talks, symposia, and colloquia are scheduled to come up at your university which you could attend, even if they are not directly related to your studies. Especially with evening or lunchtime talks there is sometimes food provided after the talk deliberately to encourage networking ("symposium" is of course Greek for "intellectual drinking party").

A number of universities have set up structures for incoming students to start interacting with each other online over the summer prior to first semester.  Some spring up spontaneously.  See what you can find. // Read the faculty, post-doc and grad student online bios from your department.  Read some of the faculty research program descriptions.  Pick a couple of papers that interest you to read.  Practice going to social events where you don't know anyone, and introduce yourself to people. Finally, learn to play volleyball and frisbee so you'll be ready to go at the welcome picnic.

Professional networking is best done in person, therefore if you want to begin networking before you start your graduate studies your options will be limited. Check various social media platforms (Facebook, Twitter, Reddit, etc.) to see if any of the researchers/students at the school have social/networking groups set up. If they do, try joining/following those groups, introduce yourself as a new student and take it from there.
Additionally, check the university website to find the list of professors in the department. Usually these lists include their email address and a link to their research websites, or a list of their research interests and publications. Identify a couple researchers whose work interests you and skim a few of their most recent papers. Then, contact them, let them know you're an incoming student and you've read their work (mention a specific paper of theirs you liked) and ask if you could meet them to discuss their work when you arrive in September. That way, you can introduce yourself to several professors and make it easier to establish contact with them when you arrive.


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Professor Ignoring Emails RE disagreement over grading
I recently had a concussion. I got everything on paper signed by a doctor, and emailed my teachers immediately. During my medical absence, I missed a quiz. I emailed my teacher regarding making the quiz up, and he said “that’s what the quiz drop is for." However, I had already my quiz drop due to the flu.
I told him I don’t think it’s fair that I have to get a zero for one of the quizzes when both instances were out my control, but he just stopped responding to me. I have followed up twice. What should I do?

Have the answers and comments on your previous question: https://academia.stackexchange.com/questions/137906/professor-doesnt-respond-to-notice-of-inability-to-take-a-test-in-his-class not given you sufficient guidance?

It’s a different issue in my opinion

Seems like you have not addressed all the suggestions and ways to contact him that were suggested in the previous SE question you posted. Having that evidence is important before you can escalate up the academic grievance pathway. Be gentle on yourself, even mild head injuries can have long term effects that are obvious to others but not yourself, get support, student welfare, etc - https://www.headway.org.uk/about-brain-injury/individuals/effects-of-brain-injury/executive-dysfunction/

The two questions do seem distinct to me -- the first seemed like a legitimate communication issue, while this one seems like the professor is deliberately not responding to avoid having to discuss this with the OP.

Does sound like more an escalation policy and how to raise grievance. My comment was whether there was evidence of other ways of proving communication has ceased and being ignored before going through formal channels which was related to the earlier question.

I do best 4 out of 6 quizzes... And students miss 3 and want a replacement... even though I am being generous with best 4 out of 6... Sometimes you give an inch and they want a yard...

Your professor has probably decided that make-up quizzes are time consuming and not worth it -- students need to learn that sometimes missing an appointment has consequences etc.
I'd recommend checking if the professor is willing to drop 2 quizzes instead of just 1 for the whole class or for anyone who has a doctor's note.
That way you are working with your professor's plan rather than negating it.

That makes sense. However for both quizzes, I deliberately did not miss them, I physically was not able to show up. That is why I’m confused what to do as I’m not sure what do in this case.

It depends on your institution. In UK universities, the process for handling multiple missed assessments due to medical reasons is normally conducted by a designated panel of academics, rather than being at the discretion of an individual course convenor.
In general, there is a limit to how far an academic course will accommodate missed assessments (it would be absurd for somebody to be granted credit if he/she had missed 100% of the assessments, no matter how compelling the reason), and it sounds like you have breached it. In your response to the issue, you need to distinguish between:

your feelings that the limit is too harsh; and
the fact that you breached the limit is not your fault.

It sounds like your course convenor has determined that he/she is willing to disregard one assessment, but not more. If the reason for that policy is that he/she considers that disregarding more than one assessment would give an inadequate evaluation of your acumen, then your best recourse may be to ask for the whole course to be disregarded, and to be given an opportunity to start it again from scratch.

Check your school's rules regarding missing assignments due to medical conditions. If it says you are excused as long as you present proof , then go to the department of the class you are taking, with the medic recipe and the mail you send the professor plus his response and they will have another professor give you the quiz. If they don't dont then escalate and go to the direction of the school.


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How to proceed when the baseline (state-of-the-art) published results claim much better performance than I can reproduce?
I am graduate student, to finish my degree I need to build methods outperform what is already there. An issue that I came across with, is that two papers reported way (I mean more than 20%) more than what resulted from my reimplementation. This could be due to two reasons:

I missed something during the implementation. Which is what I have been telling myself. For months, I tried all possible combinations and possible paths. One of the methods is straightforward. Still, I could not reach their claimed performance.
I contacted the corresponding authors, and no one replied. So I tried to contact the other authors.
The first paper, the author replied and sent me the code. He/she told me to keep all details ”confidential”. Well, it turns out they they are not using the data they claim in their the paper, of course their results are different than my reimplementation. And my implementation was correct.
The second paper author also replied and they didn’t send me the code because they say it is easy to implement, but confirmed that what I did is correct still I couldn’t understand why such difference.
Both papers are published in <2 impact factor journals. Their web servers are not working.
They are not honest.

Now I am stuck, my method does outperform my reimplementation of their methods but not what they claim. The first paper I can’t say anything because “it is confidential” the second paper I can only confirm that I correctly implemented their method for the most part  (based on my chat with the authors)
I know that I probably could not publish on this part of my work, because who is going to believe a young scientist who just started her way?
 But not sure how the committee are going to believe me. What can I say or do? Please help me

What is the measure of “performance” you are referring to and could its definition be the source of this 20% difference? As a software developer I’m used to seeing such performance gains (in runtime) just from small but significant optimization tweaks, that are very platform dependent.

It's the core of real science to publish results that are different. After all, if you got the exact same, then nobody would have learned something new.

@Gloweye: I'd qualify that seeing that whole fields (life sciences) are currently suffering a severe lack of replication.

I can't really dispute that. But it's still proving the old theories wrong that make progress. Like when we figured out Newton's force/acceleration ideas really are insufficient when approaching c. What I'm trying to say, don't be afraid to disagree.

Before assuming the worst (academic misconduct), you could take into account that there may be subtle differences (optimiser? better data structures?) that caused the 20% gain. I once had a student code something, and, for fun, coded it myself. My code was a factor 1000 faster for a more complicated case. It was not just the language, but the way I coded it that gained this speedup. Of course, if their code, with the original data, does not run that fast with you, then there may be something wrong. Again, it may be an honest mistake rather than misconduct. Report your results as side remark.

3rd option: they made an honest mistake.

As others have said, go ahead and publish. The nice thing to do was to contact the authors - you did. (You could even reference "personal communication".) - So if your method is better, go ahead and publish it, just be sure to be courteous in the paper and give credit where credit is due.

Quite aside from the tweaks in the specific implementation as @mvds mentions, a 20% performance difference could easily come from differences in your hardware, OS, compiler, or any other part of your development/testing setup.

"it turns out they they are not using the data they claim in their the paper" Can we get some more details about this (or more emphasis of that particular part of the question)? The answers I've seen so far seem to assume that the differences might be due to an honest mistake, but If they published results on one dataset and claimed they were for another, that needs to be reported to the editor.

@mvds I meant the prediction accuracy, not the running time. I totally agree with the running time and optimization.

@Ray they stated that they used the benchmark dataset from bla bla,but they did not (based on what sent to me by the author). At some point someone messed up the data (removed difficult samples + add other ones) and the author who sent me the code had absolutely no idea. He/she told me that the supervisor provided the data (!)

You should run your code with the same data they used to claim 20% improvement. If you get the same (or nearly the same result), you'll have strong reason to conclude that the method is not robust to variations in the data.

@Monii_80 ok that’s indeed a different thing and the reason I asked ;-) There are still some platform dependent factors, such as the size of floating point types and handling of denormals, and maybe other rounding rules depending on choice of language, but to account for 20% difference for the exact same algorithm, I don’t know... it would be a stretch.

@Monii_80 to elaborate a bit more: in case this is an algorithm involving a lot of steps, it might just be that intermediate results are stored in a database or transferred between processes with limited field widths. Also possible: an accidental rounding of intermediate values (e.g. unintended cast to integer). I’ve seen such things make a real difference in prediction algorithms, not clearly visible at first but the reason why end results did not add up. Such issues can easily cause a 20% uncertainty in the end result. Also very hard to track down!

There is absolutely no reason that you can't publish a paper that says "We compared our method to methods X and Y. Since code the original code was not available for X and Y, we reimplemented the methods to the best of our ability. The code for these reimplementations is available in supplementary files A and B. Our new method out performed the reimplementations of X and Y by z%. However, it should be noted that it was not possible to reproduce the reported results for X and Y. "
People who want to know will have to look at your re-implementations and decide themselves if they think you have correctly re-implemented.
Seniority has nothing to do with it - be transparent, and the world will judge if they believe you or the people that won't release their code.

So much this. We need to encourage reproduction attempts in science. If OP made a mistake, then hopefully someone will find it and (kindly) point it out.

Honestly, I would also be tempted to write a short, slightly passive-agressive, statement to the extent that it was impossible to extract the original implementations from the authors. This kind of thing should be frowned upon much more than what it currently is.

In an ideal world, this. But do check with your supervisor/colleagues/... to find out the nature of the beast: If one or more of them are relatively understated but vindictive people with actual power on the small sub-field you're specialized in, this can be bad (as they and their friends will review both your papers and applications, and reject). If they're vindictive cartoon villains, you'll reap more sympathy than rejections from journal editors etc. If they're scientists, they'll respect you more for standing up (with facts, not principles).

Indeed, make your own research fully reproducible and submit it. Make sure to inform the editor of the journal you publish in about whether you want to exclude the authors of the works in question from being referees. While they seem to be helpful now, they might bias the reviewing process. They might well not, of course, but it is better to stay on the safe side and aim for affiliations that are genuine third parties. Peer reviewing is not a perfect system, but it is the least imperfect, always perfectible and the dominant one anyhow. Your community will value your contribution.

What I fear is that the paper is rejected because it lacks the "novelty factor".

@thermomagneticcondensedboson Your method seems to be a novelty, and that would still count even if the performance is only about as good as the old method

As per the question, the OP should note that the data in the original papers was not used for the results - hence the issue with results differing  By publishing his data and results, others can confirm his method correct.

Although solving a previously unsolved problem will lead to a higher impact publication, its still perfectly possible to publish if you can demonstrate you have a better method for solving an already solved problem. My suggestion would be to make your method readily available to others. Have proper and reliable installation procedures across a range of machine types, a proper interface (either a well documented and logical API, or a proper CLI/GUI) and well thought out documentation, quick-start guide and tutorial. You will then be the only purveyor of a useful solution.

I would go even further than this: your method does not have to outperform anything, it just has to be different to what has been tried before. (Within reason). If you have a novel method that outperforms accepted methods by 20%, that's extremely valuable because people will use your method. But if your novel method underperforms by 30%, that's also useful because others doing research in the same field will know to avoid your method! Don't be afraid of "proving a negative" - yes it's not as glamourous, but a signpost saying "there's a hole in the ground here" is still helpful to others.

@IanKemp in theory yes, that's valuable or should be valuable; but in practice in many fields e.g. machine learning it does make it much less likely that your paper will be accepted in competitive venues.

People can be dishonest. They can also make honest mistakes and publish bad science. Don't assume that it is you who has an inferior result. And don't assume that a doctoral committee won't believe you. If they are competent to judge you without the earlier results they should be competent to understand what you have done.
However, I have two suggestions. The first is to walk through what you have done with your advisor and/or another faculty member who is most competent to understand your work. You may, indeed, have the best results. If you can get support there, then the larger committee should be no problem. I don't think that you need to hide the communication you got from your committee members. It may be necessary to explain why you can't believe the reported results from the other paper. I don't think that "confidentially" really applies here.
But the other is a bit harder. See if you can figure out exactly where the other group failed to match their methods to their results. If you can do that, then you have much stronger evidence for your own work.
The evidence you mention here seems pretty strong to me (an outsider) that the other paper has a problem. There is no reason not to contradict it if it is incorrect, for whatever reason.

There is alo another case: they found something which they interpreted as "numerical mistake" and dismissed it -- to get back to traditional results. They were not experts in numerical methods and honestly thought that what they saw was a glitch in the computation. Turned out their model was not good enough, the "glitch" was a hint about that and someone else built on their work to expand the model. They were not very happy with the outcome but were credited for the unfortunate smoothing of the result which led to the discovery. Happened in 1995 :)

to finish my degree I need to build methods outperform what is already there

No, that is not true. You need to deliver a piece of proper scientific work and advance knowledge and that does not depend on what direction your findings point.
Of course, things are easier and more pleasant if your implementation is better. But the actual scientific part of your thesis is to study both the old and your approach scientifically and then conclude whether one is better (and possibly in which situations).
The difficulty in your situation is to proove that the discrepancy to literature is not due to your incompetence or lack of hard work (=> you deserve a bad mark) but actually due to "nature" not being as it was supposed to be by the previous paper.
What you can and should report is

that you were not able to reproduce the findings in papers 1 + 2,
in consequence have been in communication with the authors.
Importandly, that your implementation has been confirmed as correct by private communication with the authors of paper 2 and by comparison with (confidential) code you received from the authors of paper 1 again by private communication for that purpose.
If

Well, it turns out they they are not using the data they claim in their the paper, of course their results are different than my reimplementation.

means that you got the data set they actually used and got the same results with that, then you can also report that for a related data set, the same results were obtained.
If not, it may be possible to kindly ask the authors of paper 1 + 2 whether they'd run a data set you send them and give you the results of their implementations so you can compare that to your results. You can then report (hopefully) that equal results were obtained on a different data set and thank the authors of those papers for running your data.

The last two points should make amply clear that the discrepancy is not due to a fault in your implementation - which is what counts for your thesis.
As a personal side note, I got top grade on my Diplom (≈ Master) thesis which (among other findings) found that the software implementation I was using did not work as it was supposed to. I was able to point out a plausible and probable reason for that bug (which may have been a leftover debugging "feature") - which is much harder for you as you don't have access to a running instance of their software that you can test (= study) to form and confirm or dismiss hypotheses about its behaviour.

As an addition to what @Buffy explained already about the possibility of honest mistakes in published papers:
As scientists we tend to work at the edge of what is known. Which also means that we're inherently running a high risk of not (yet) knowing/having realized important conditions and limitations of what we are doing.
We thus also run a comparatively high risk that tentative generalizations we consider may turn out to be not all that general after all. Or that we may be plain wrong and realize this only later (or not at all). I believe it is very hard for humans to be completely aware of the limitations of the conclusions we draw - possibly/probably because our brains are "hardwired" to overfit. (Which also puts us into a bad starting position for avoiding overfitting in e.g. machine learning models we build)
The take-home message from this is that we need to be careful also when reading published papers: we need to keep the possibility of the paper being wrong, containing honest mistakes or not being as directly applicable to our task at hand as we believe at the first glance in mind.

I missed something during the implementation.

I experienced something similar once when I was also implementing a reference method from literature (related but different field). It turned out that different defaults in the preprocessing of the data caused the difference - but only after I had the bright idea of trying out to omit a preprocessing step - although the model doesn't make much sense physically without that step, but the paper didn't mention any such step (neither do many papers in my field who do use that step because it is considered necessary because of physics).

They are not honest.

While that is of course possible, I've seen sufficient honest mistakes to use Hanlon's razor (which I first met as Murphy's razor): and not assume dishonesty or misconduct unless there are extremely strong indications for that.

Proving superiority may in any case be something that is impossible due to limitations in the old paper.
E.g. if they report validation results based on a small number of cases, the uncertainty on those results may be so large and thus it cannot be excluded that the method is better than it seemed that truly improved methods later on will not be able to demonstrate their superiority in a statistically sound manner.
Still, such a shortcoming of the old paper does not limit the scientific content or advance of your work.

Too bad I can give only a single +1. Publishing negative results, i.e. proving that something does not work, is very important as it saves other scientists going down a path that does lead nowhere.

Citation of Hazon's razor relevant and valued: Never attribute to malice that which is adequately explained by stupidity

@XavierStuvw you mean Hanlon's razor?

@slebetman Thanks, I did as per the link target, but it was too late to edit when I realized that my wires were crossed. This note lends the possibility to add a corollary or identify a premise: malice and stupidity can be equally obnoxious

You can write that you used your implementation of the competing method for your results, and that you were not able to reproduce the published results. Make your code available so people can check.
It seems that the authors of the other papers didn't publish their code, so nobody can say you should've used that.

In the first place, you should consult this with your supervisors. The code for papers is often rushed and unfinished, and what works on one machine may not work on another for a number of reasons. The most reasonable way is to let your supervisors know that you implemented both methods, communicated with original authors (mention only non-confidential things/say some things are confidential/ask authors for permission to discuss the implementation with your supervisor), and yet you did not reach the performance claimed. As a senior academic capacity, they are better equipped to decide what to do with regards to politics of department/field/research teams, are bound to get quicker and more elaborate responses from authors of the papers and handle potential fallout should anything go wrong in the process. I would not advise to pursue this matter on your own, and surely if you have doubts about something this important to your project, it would be reasonable to seek their advise and they will understand that.

"ask authors for permission to discuss the implementation with your supervisor" 'confidential' rather means that the code you should be published.

I think that @jericho-jones answer is the most appropriate one.
Also, take this as a lesson. Use good coding practices. Check your work into a source code repository. Tag your working code so you can easily back out changes. Make everything reproducible, including processing any raw data. Make the raw data read-only using file permissions, deny database update/insert/delete statements, etc. Make sure your code builds every day, or every other day. Make sure a change builds and passes all tests, and reproduces already known results before checking into your source code repository.

In addition to the other answers, you should consider publishing your re-implementation. Then any reviewers can check if they think your results are plausible or if they spot a flaw in your re-implementation.
In the first case, it is right to say "We implemented paper X, but could not reproduce the claimed efficiency" and in the second case the flaw found by the reviewer may help you to improve your re-implementation, so you achieve a similar result.
Most reviewers will not debug your code, but you did your best efford to allow anyone to verify your claims of less efficiency and at least your paper is as honest as possible.
If the algorithm is interesting, publishing an open source version may get some users, that point out issues with your code (or contribute improvements) as well. But make sure not to be too close to the confidential code, as the original authors may claim copyright infringement.
You may use clean room reverse engineering with another person or at least do it yourself by just using the given code to write down the parts missing in the paper and then reimplement it from the documentation and not from the code.


text:
Changing research topic
I am a Master's student doing a research internship. I do not have to defend the results of my research at the end of the internship; however, I would ideally get a publication out of this.
I am working with a well-respected professor in applied mathematics. When he suggested a research topic, I quickly agreed. At first I found the topic a little bit strange and uninteresting, but I thought that I didn't understand it well. It seemed like he had not thought about the topic a lot, but he seemed enthusiastic about it.
After learning more, I confirmed my initial impressions. This problem has been studied extensively before, and my special case doesn't seem all that special. I reported my findings to the prof and he seemed to agree. Yet he suggested that I perform several numeric experiments to further investigate. I did so, but didn't get any definitive results. My supervisor had said that if the experiments failed, we would change the topic. But now he just keeps suggesting new ones.
I do not like the topic and would like to change topics as quickly as possible. However, I do not want to be seen as one who just jumps from topic to topic when something doesn't work. During our last meeting I attempted to "corner" the professor, asking for the interpretation of the possible outcome of the experiment in advance. Also, I was seemingly displeased by the idea of more experiments because I was arguing about their meaninglessness. The supervisor said something like "you seem to be looking for the excuse to not do the job." I definitely want to do the work as I want to get results. Yet, I think our current efforts will be fruitless.
What should I do?

First, negative results are useful too: for instance if you can show that the method is inapplicable by trying every reasonable option to make it work, this is a result by itself and it might be worth publication. Since your intuition is that this method cannot work, why not try to prove it? It doesn't really matter that your supervisor doesn't share this intuition.
It's not very surprising that your supervisor insists on continuing in this direction since it was what you agreed to at the start. Now if this is not something you want to do, then you have no choice: you need to talk to them about switching to a different topic. Mind that it's not always feasible to switch to a completely different topic during the course of a research internship.
Without knowing the details, my impression is that it's probably in your interest to finish the work that has been started rather than restart something from scratch (I assume that it's only for a few months). The important lesson here is: don't accept a topic that you don't like in the first place. Keep this in mind if you choose to do a PhD later ;)

Having been on both sides of this kind of thing, I suspect there is a mismatch.
It sounds like your professor is thinking something like: "A is a well-studied problem. B is a subset of A and has certain interesting qualities. So, we should be able to study B and achieve C." Your response so far has been: "nope, I've studied B but can't achieve C."
There are only two options. Either:

Your experiments will eventually succeed, or
There is some interesting reason why C will never be achievable.

Those are the only two options, and they're both interesting (perhaps even publishable) results. So, I suspect your professor is confused why you want to give up without achieving one of these end states.
Which is not to say your professor is necessarily right. It's totally possible that  you've already found the "interesting reason why C will never be achievable" and your professor should have called you off. In this case, it sounds like a communication issue -- either he doesn't understand your results, or you don't understand his goal.

There’s a third option: C has already been determined to be (un)achievable by someone else.

I'd say that's covered by the current options -- If someone else has done it, then OP's experiments should succeed; if someone else proved it's impossible, then that's an "interesting reason" why C is unachievable. Though obviously I take your point that OP should do a literature search to avoid duplicating/wasting effort.


text:
How to solve problems in state-of-the-art work with introductory knowledge
Edit:
I consulted with the professor regarding this matter today. (The reason why I didn't ask before posting the question was because I feared I might have lacked some understanding --- I mentioned I heard very few fellow students expressing the same concern)
She confirmed that even if a result was not quite successful at length, a group of students can still be deemed as having satisfied the assignment's requirements, so long as the students have tried to apply the knowledge in a state-of-the-art problem, which is proven by the report as well as the prototype submitted.

I am having an introductory taught module in Computer Systems Security in my last year as an undergraduate student. To clarify, its name or module handbook does not say it is introductory, but it has been arranged as the first module dedicated to Computer Systems Security in my major. It is a taught module because we have lectures and labs, and a final exam.
In a group assignment of this module, the professor has tasked us with

In this group project, students are required to do a project related to computer systems
security. They have to choose a topic and then study the state-of-the-art work done related
to that topic. Identify the shortcoming of the existing work and propose an enhanced
security solution using technologies learnt in this module.
In particular, each group should

Realize a prototype and elaborate the complexity of implementation;
Write a report in IEEE conference paper style A4 ...

, which in my understanding requires us to use the introductory knowledge taught to solve state-of-the-art problems (although we only need to propose and realize a prototype, and not need to evaluate and verify our solution).
This is too difficult in my opinion, but very few fellow students expressed similar concerns with me, so I haven't been confident enough to consult the professor with my concern. In addition, I learnt that the assignment of this module in the previous academic year has been effectively the same.
I do have some tentative ideas on how to do this:

read some state-of-the-art papers, and according to their Limitations, Related Works and similar sections, just paraphrase their description of the problems of their work
then, because the knowledge taught in the module is quite broad, just apply some related concepts anyway without describing the details (because I am not able to), and conjure a prototype based on that without knowing exactly if it's right or wrong.

However, this approach feels like academic dishonesty to me. Nevertheless, since the past year's assignment tasked the same requirements and I haven't heard of complaints from past students, I believe at least most students must have had an idea that I have not thought of. Therefore, I want to ask if there's any way to complete this assignment correctly without using something that is not so genuine like that idea of mine, given the background that I have described.
In particular, any idea for this would be very helpful to me:

how to find state-of-the-art problems that can be truly solved with only such introductory knowledge.

I'm not sure the learning exercise is to solve state-of-the-art problems. Rather, it seems to be about the analysis and critical thinking to attempt to understand and solve such problems. Even just thinking about your bullets 1. and 2. should teach you a lot.

@MisterMiyagi Hello, thanks for the comment. By "your bullets 1. and 2.", did you mean those in my tentative idea or those in the project requirements?

The tentative ideas. Sure you can half-ass that, but that applies to most assignments. Simply deciding which "Limitations, Related Works and similar sections" are worth it and deciding which concepts might apply should teach you a lot.

@MisterMiyagi I see. I thought writing a prototype that I only think will work in a report was academic dishonesty. It is such a relief that the action is not considered one. Since your comment answers my question, you can turn it into an answer and I will accept it then.

Ah, sorry, I can't actually answer "how to find state-of-the-art problems that can be truly solved with only such introductory knowledge", only give you some ease of mind.

@MisterMiyagi Yes, but if what you say is true, then I won't have this question to answer. Therefore, your comment effectively solves it.

To concretely agree with @MisterMiyagi, I once had an MSCS-level final exam (!) ask effectively, "How would you solve this open problem in compiler design?"  I was lucky that I already knew it was an open question in the field, but the takeaway is that the question did not expect a full or perfect breakthrough answer.  The question wanted me to show where my knowledge ended, and what I did when I reached that point.

Also note that your quoted text does not ask you to solve a state-of-the-art problem, only to propose a solution.  There is a vast difference between the two.  You can absolutely show you have mastered the introductory concepts by trying to use them to solve a problem above your paygrade, even if the attempt is unsuccessful.

Emphatically, as in other comments and answers: no, you do not expect to solve long-outstanding, important problems with only introductory-level understanding. The genuine goal is to engage with serious problems, and start to realize why they are both important and difficult. "Trying and failing" is an important educational riff. :)

@Anonymous Could you elaborate more on the differences between "ask us to propose a solution" and "... to solve"? (I already know what the prof. probably meant by your and other people's help, but I want to discuss the phrases here). In my opinion, to propose a solution, at least I need to believe what I proposed can solve the problem according to the theories. The only difference is to apply the solution or not, but the professor asked for a prototype (she also asked to include code in submission). I would say "students need to try to solve/propose a solution" for better meaning.

@GuanyumingHe not in the space of a comment and not, due to prior commitments, in the next 24 hours.

The short answer is: "You don't". But maybe you get lucky - extremely lucky. Unless a field is extremely new, with few practitioners, those with experience have almost certainly covered all the ground that can be covered with only elementary knowledge.
But, you need to start somewhere, and the place to start is to examine what is already known (state of the art) and then make speculations or hypotheses about solutions and extensions. Many (probably most) of those will be untenable, but it is still part of the process.
Insight into a field is gained from practice. Once attained that insight can make it more likely that your hypotheticals have some merit. But you need the insight and therefore the practice.
I think your exercise is to get you to think in this way and make some hypothesis and then use what is known to examine it critically.
This isn't special to CS, by the way. Many fields use this process to come to things that might be true or are likely to be true (requiring insight) and then gathering the evidence that proves them right or wrong.
I think the professor's expectations about what you can actually accomplish are low and they are more interested in how you attack the problem. Do so honestly and critically. How is some hypothesis supported or not supported by the evidence? Make the "proposed solution" reasonable and then examine it critically.

Thank you. I had focused too much on the word "solution" and thought the professor asked us to solve problems. Through your answer and other people's comments I see that like you said the prof. probably just expected us to exercise our knowledge in state-of-the-art work rather than to really accomplish something.

Therefore, I want to ask if there's any way to complete this assignment correctly without using something that is not so genuine like that idea of mine, given the background that I have described.

The way the assignment is worded, I think this is very possible.
To begin with, I think the word "solution" in the assignment should be understood in the context of "security solution", i.e. a solution to the general problem of securing something. By my interpretation, you are not expected to identify and solve any specific open problem in a state-of-the-art system. I think your project would fulfil the assignment if you produce a prototype system which does not have that problem, even if it has other problems which the state-of-the-art system doesn't.
After that, even identifying shortcomings of state-of-the-art systems in cyber security does not necessarily require advanced knowledge of cyber security. A shortcoming doesn't have to be some technical vulnerability that requires a specially-crafted network packet to exploit. Consider remote authentication: the state of the art is to log in using a password, and add an optional extra authentication factor such as receiving a one-time code by SMS. This system has many shortcomings:

Users choose passwords which are weak and/or guessable.
Users write down their passwords, or share them with other people.
Users are vulnerable to keyloggers or other malware which can steal passwords.
Users forget their passwords.
Users don't enable the second factor.
One-time codes received by SMS are vulnerable to SIM jacking, where the attacker tricks or bribes the phone company into transferring the victim's phone number.
One-time codes are still vulnerable to real-time phishing attacks.

Notably I didn't have to read any state-of-the-art research to think of any of these shortcomings. The state of the art in cyber security is generally that we have systems that are good enough most of the time, not systems that are perfect as far as we know.
And these shortcomings aren't even things we don't know how to address; all can be addressed by known means (e.g. password policies, mandatory 2FA, FIDO hardware keys). The reason these alternatives aren't widely used is because they introduce other shortcomings (e.g. user dissatisfaction, cost).
Security is all about trade-offs. By my reading, your assignment is fulfilled if you identify one issue with an existing system, and build a prototype for a system where you trade that issue off for a different one; your system doesn't have to be better overall in any measurable way. This can definitely be done with only introductory knowledge of security.
All of that said, since you are unsure and I'm not your professor, the best thing to do is approach your professor and ask for clarification. If you have an idea along the lines I outlined above, check with your professor whether your proposed project would meet the requirements.

Thank you for pointing out the trade-offs to me. What I had in mind was building a prototype stronger than a state-of-the-art one, so I felt that was very difficult.

I think that your confusion is just a misunderstanding of the sequence of tasks that they are asking for.  From reading the part of the assignment you posted, I would paraphrase it in steps as:
1) Come up with a security solution of some sort, solution here meaning a mock product or piece of software.  The topic of the solution, or software product, must be related to the security of a computer system.
Some examples might be:

Program to store passwords securely.
Code to detect if a request to a web server is from Googlebot or not.
Web browser program that saves absolutely no files to disk, so there are no clues to it being used or what sites it visited.
A program that sends or shows an alert if a certain file is changed.
Any computer security product on the market that you can make a simplified version of.  I looked on Amazon and just typed in 'computer security product' and the first result was a glue-on padlock, so maybe it could even be a physical security solution like that which you could make a prototype of.

2) After building your solution, or product prototype, you will then study what is being done related to it that is state of the art, or the most advanced version of what your product is and what new or novel solutions to the problem that your solution/product solves are being created.  After studying what others on the cutting edge of that type of solution are doing, see if you can identify a shortcoming or flaw in your design that is addressed by others on the cutting-edge of that same type of solution or design, maybe something extra that they secure against that you had not initially thought of when making your prototype.
3)  With your prototype and your list of potential flaws in your solution, or the weak spots of your security solution that could be improved, see if you can apply the things you are learning in the class to those weak spots or shortcomings in your solution and use what you have learned to improve upon them, and write a proposal of how the improvements to your solution might be implemented.
I hope that makes more sense, I understand how the particulars of the grammar can be kind of critical when you are deciding whether to commit to a class or not so it is natural to want to know the precise specifics of the project requirements, but from what I can see I think what they are basically saying is that you will be creating some type of security solution, and then through what you learn in the class and from studying the topic of your solution identify something that can be improved, and then write a proposal describing how that improvement might be implemented.

I appreciate your detailed suggestions very much. Though according to today's consult with the prof., I believe she meant for us to study some work first and come up with a solution later.

You are welcome, and do not fear or worry that you are asking too many questions to the class or teacher, I would always try to decipher things on my own to avoid asking because it made me nervous but its makes life 100x easier if you just get it out of the way and then you don't have to think about it.  I hope you enjoy the class, it alerady sounds more fun than mine... we had to make a command line ATM machine over a year on the terminal lol... :)


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Does mentioning why I took a break after my MSc affect the chances of admission?
I had taken a year break after my masters, before seriously applying to PhD programs. The reason was that I was stressed and burnt out, especially during the COVID times, and wasn't ready for a PhD directly after 5 years of university, and felt like I should take a break. Even though I had applied to 1-2 places due to the insistence of my parents, and for the sake of easing my conscience that I wasn't applying anywhere, I did not put my heart and soul into the applications like I would have liked to.
Now that I have taken a break, and have had time to think about it, stay away from mathematics for a while, or have read some mathematics that I like in my own pace (Nothing hugely substantial in terms of my future plans, but I think I really needed the break, to clear my mind.), I am applying this academic cycle.
The problem is, one program asks me for any situations or circumstances that I had for any significant gaps in my education period, and they claim that the information is voluntary and will not affect my application negatively. But as I understand it, they probably mean reasons like an illness, family emergency, having a child, or some other emergencies that could not be avoided. I am doubtful whether taking a break because I was stressed (of which I have no concrete proof, than my own feelings), or was unable decide whether I want to continue with a PhD then (but the fact that I want to, is as clear as day to me now), would count as a valid reason.
Moreover, I do not understand how can something be used to better understand the application, but still not be used negatively.
So, do you think it's a better idea to mention these reasons in the application, or would it be better to avoid it altogether?
Also, seems like this question would be similar to mine, but it does not address what to do if the program application asks for a reason for the break.
Edit : I realize that I might have potentially miscommunicated the time I got out of university. It was in this (2022) summer that my masters got over. When I mean I took a year break, I just mean that I would only be admitted in the coming fall, and hence it would be a year after my masters; and not that one year has already passed after me finishing my masters.

What about "due to COVID I decided to take a break for a year"?

What were you doing in that time?

@Yanko Are you suggesting me to use that as the reason? It is a good idea, and it would not be exactly untrue (Yeah, COVID did affect me, but not because of being unable to travel, but because of the some of the courses and the stress during the time.) but for the fact that the COVID was almost over by the time I completed my masters.

@Yanko Also, I realize that I might have potentially miscommunicated the time I got out of university. It was in this (2022) summer that my masters got over. When I mean I took a year break, I just mean that I would only be admitted in the coming fall, and hence it would be a year after my masters, and not that one year has already passed after me finishing my masters.

@AzorAhai-him- Read a paper related to my thesis, completed a chapter of a textbook that I always wanted to read but could not complete during my coursework, attended two workshops - one related to my field, one that is not so much related to my field but that I still find interesting. I also applied to attend a mathematics conference, and I was able to listen to (some of) the lectures in the conference online.

Maybe nothing too heavy, as I said. I just kept up with doing some mathematics I found interesting, but nothing too substantial to, say, write a paper on it, like the answer here says.

@Ron Haha, no I mean besides math. Did you work?

Describing the break as caused by "burn out" is probably a mistake. It is a common enough practice that it probably needs no explanation unless asked. But "I felt I needed a break to consider options" is better than "I was burned out from five years of study."
If you let the break go on too long, however, it makes applying more difficult as you start to lose contact with potential letter writers.
And, if there are still mental health issues, talk to a professional so that you develop a coping plan that will carry you through several more years of intense study.

But as I understand it, they probably mean reasons like an illness,

Stress, particularly stress which prevents you from working or studying effectively, is a health condition. Burnout is also a health condition.
For the purposes of your application, it doesn't really matter that you don't have a formal diagnosis from a doctor or a psychologist. They wouldn't ask to see evidence of a diagnosis anyway; if they would demand you to substantiate your answer, then this field on the form wouldn't be voluntary.
If you were asking for adjustments (e.g. more time on exams) due to a health condition then that's when you would be asked for evidence.

Moreover, I do not understand how can something be used to better understand the application, but still not be used negatively.

The main things they are interested in here are:

If your interruption was caused by something that could happen again during your studies, and
Whether your interruption has left you "out of practice" with the subject, or if you have maintained your knowledge and skills in the meantime.

It's difficult to say what process they might have in place to ensure that information you provide about it doesn't negatively affect your application. There are several possible processes; the simplest is that they could just "assume the worst" if you don't write anything, but in that case they should warn you that leaving the field blank could harm your application.
I could speculate about other possible processes they might have in place, but I doubt that would be helpful. It certainly seems like there are things someone could write in this field which would be worse than not writing anything, which it wouldn't be rational for the institution to ignore. For an extreme hypothetical, if an applicant wrote "I spent five years in prison for maiming my previous supervisor, Dr. Smith" and Dr. Smith worked in the department they were applying to, I sure hope it would negatively affect their chances of admission.
So I wouldn't take the words "will not affect your application negatively" literally, rather I think what they mean is that if there is anything in there which might suggest a problem for your future studies, but you're an otherwise promising applicant, then they are more interested in working with you to prevent that problem, rather than rejecting you.

The problem is, one program asks me for any situations or circumstances that I had for any significant gaps in my education period

Not going straight to a PhD after your master's is not a "gap in your education period." Especially not if you went straight into the master's after undergrad!
It sounds like they are asking about if you had to take time off in your undergrad or something. I don't think this applies to you and you don't need to answer it with an explanation about burnout.


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Any suggestions how to deal with stress if stuck while working on master's thesis?
I am doing my master's thesis in Physics and I am stuck already for a quite bit. What I mean by "stuck" is that I am trying to implement something completely new to the analysis of my working group and I have put REALLY MUCH effort into it and now once this thing is finally implemented, the results I get do not seem reasonable.
I do understand that communication in research is very important and I do communicate with my supervisors, however they do not seem to have answers and I am in an infinite loop of suggestions what I can do to improve this - without any improvement. What stresses me the most is that I was planning to ask my professor to continue with PhD in his working group, however now I do not feel "worthy" anymore. I feel like I failed his expectations and honestly, not only his, in the first place - I failed my own expectations. I know that it may sound like I judge myself too harsh, however I am stuck with this problem already for 3 months and it transformed into literally infinite loop of trials to find what's wrong and "neverfinding" answer.
I wanted to share this failure of mine and hear some suggestions from you (maybe from someone who has gone through something similar) on how to overcome self-judgement and stress.
Days can pass with me looking at the screen and my code, with trying million options and going through one failure after another.. It starts to hurt me and I am scared..

Studying is easy. Research is hard. You just made the transition. Check with your supervisors what your options are in case the direction does not work out. This is not a PhD, so there is no expectation of novelty, and a negative result may be acceptable if you demonstrate that your methodology was solid. If it were a PhD, I would set myself a limited time window and, if it is not solved by then, cut my losses. Also, can you create contact with experts in the method?

I don't see a question we can answer.

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.

Is the problem in the implementation just having a bug, or in the algorithm you have implemented just being unsuitable to the problem?

Related, possibly helpful: https://academia.stackexchange.com/questions/30995/what-to-do-when-you-spend-several-months-working-on-an-idea-that-fails-in-a-mast/31082#31082

3 months is very little, at least in math (I do not know about physics) and at least for a hard problem. Several years would be normal. Try looking at the problem differently,

Thank you for your response. I thought my question was obvious and I did not explicitly ask it here. I am interested in your suggestions, how to deal with the stress caused by the reasons I have mentioned in my original post. I have also to mention, that I would evaluate myself as a strong person, I usually face the difficulties bravely and work on solutions, however now it gets tougher, since my power of will seems to be fading away..

Does this answer your question? How should I deal with becoming discouraged as a graduate student?

You should google impostor syndrome. These feelings that you are not "worthy" are very common. (You are worthy, your advisor is doubtless very experienced, knows what they are doing and would not have chosen you as their student if they didn't think you were "good enough".) I don't have a cure, but knowing that many (most?) researchers feel the same may help. Keep going!

Thank you @astronat ! I will google about that syndrome and hope I will find ways to deal with it.

@WolfgangBangerth , the problem is that neither me nor my supervisors know the cause of the problem and we have to investigate this together (they or me suggest different things and I implement them), and this is fine, this is how the research works, I understand, but I am stuck for a long time, which maybe does not seem a long time in general, but as a master's student, I am pretty much limited in time..


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How to publish for top journals?
I am a Software Engineer and I have finished BSc in Computer Science and Engineering, currently I am working as well as doing some researches related to Computer Science in my spare time. I am curious to know how I can publish for top journals as a  single contributor without any mentors or supervisors. I will highly appreciate if you provide any feed back or reference related to the process.

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.

Writing well is one prerequisite -- e.g., start by fixing the many grammatical mistakes in your question!

Thank you for your suggestions I highly appreciate it

Before you ask about publishing in top journals, why not ask about doing great research?

Thank you very much I will ask about it in a seperate question. Currently I am in the process of defining scope of the research work.

The answer is simple but difficult.
Write the best papers if you want to publish in the best journals.
There is no bar for being an individual. Do good work. Write it up properly and submit it.
But, to write good papers without any guidance is very difficult. There must be something "real" behind the papers, usually research and that, alone, is difficult. The first paper is the hardest. It gets easier after 10 papers, or so. But if you submit a lot of poor papers to top journals you might get a reputation as a crank. Having people to bounce ideas off of can be a big help.

Thank you very much for your valuable guidance . I highly appreciate it.

'good work' -- difficult to appreciate if you do not have a teacher who produces 'good work' him/herself.  Even harder if you do not have the skills to construct said 'good work'.   'Write it up properly' is also a hard one.  Again, you need a good teacher who can write well.

Reading many papers from top journals and some below that level consciously may help, and then asking yourself whether you understand why those in the top journals ended up there, and how your own work compares to this.