₦airaland Forum

It is no secret that the issue of improper dressing among students is a growing concern in schools today. From sagging pants to revealing outfits, it has become all too common for students to dress inappropriately. You must have noticed the increasingly high rate of improper dressing among students in schools. It is a growing concern that has become more prevalent over time. Dressing appropriately is not only a sign of respect for oneself and others but also an important factor in promoting a positive learning environment.


As parents, teachers, and guardians, we want to see our children and wards dress appropriately, and present themselves in the best possible way. Unfortunately, the reality is that many students struggle to understand what is appropriate for school, leading to a wide range of dress code violations. We know how hard it is to convince children to dress properly. They might think it's not important, but it's not just about the clothes they wear. It's about creating a mindset of respect and responsibility, which will carry them throughout their lives. This is why we are introducing our software to help prevent improper dressing in schools.

To address this issue, we are developing a software that can detect and prevent improper dressing among students in schools. The software is designed to identify clothing items that do not conform to school dress codes and alert the appropriate authorities. With your help, we can build a platform that ensures our children and wards dress appropriately and reduce their exposure to people who dress improperly.

Imagine being able to prevent your child from being exposed to individuals who dress inappropriately, or from being a subject of ridicule from peers for dressing improperly. With this software in place, you will be able to do just that.

We need your help in building this project as a project of this magnitude requires a collective effort [think chatgpt]. That is why we are enjoining you to participate in the data collection process. By taking images of students dressed inappropriately, you will be helping us build a comprehensive dataset that will enable the software to accurately detect improper dressing. Your participation will directly impact the quality of the software and the effectiveness of its results. The data collected will be treated with utmost confidentiality, and all participants will be credited for their contributions.

We understand that as concerned parents, teachers, and guardians, you want to play an active role in ensuring that your children and wards are safe and secure. By contributing to this project, you will help build a safer environment in schools, reduce the exposure our children and wards may have to people who dress improperly, and promote positive values of discipline and respect for authority.

Together, let us work towards creating a better future for our children and wards. Join us in this project and be a part of the solution.

Let's join in promoting the dress codes in our universities, talking and complaining about it would solve the issue, you can now be part of the solution

Upload images at
senai.pythonanywhere.com/dress-code

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I have reached the stage in my programming path, where I truly hold high respect for those developers who meet up deadlines, most especially when those deadlines are set by people who are non-programmers and they set it on their “idea” of how long the project ought to take.

As a voluntary project for my church, I am building a multi-functional application that is a text editor, audio transcriber, a search interface capable of both keywords, semantic search (and by His Grace, soon to be added a text summarization capability).

In a bid to spread the [url]Truth of the Gospel[https://redchairtruth.com/]of our Lord Jesus Christ, which has been almost overshadowed by false doctrines as forewarned by apostle Paul in Galatian 1: 6–9; 3: 1–3 [The whole book of Galatian was written as a warning to the Church against believing people who preach false doctrines with fair speeches — Romans 16: 17–18].

We are working on transcribing audio/video messages to be able to share them with more people, and seeing how tasking and extremely time-consuming manually transcribing an audio/video message is [an hour audio/video can take over 6 hours, or for most a whole day to transcribe, and there are audio/video messages that are over 10 hours, try imagining how long that will take], even when Google Gboard speech-to-text functionalities is used.

In attempting to try to make the process easier, I made my developer skills and intentions known and I was tasked with concentrating on building the application but focusing on only implementing the audio/video transcriber function, as that is what is sorely needed and was given 2 weeks to complete it. I had it completed in 3 weeks, though I have nightmares of hidden bugs that are in the application.

Though most of the application buttons are unresponsive, as their functionalities have not been implemented. I am seriously excited of my achievement, the application was able to transcribe 10-hour audio in 2 hours, leaving to others the easy task of just punctuating the transcripts [Something I am planning of automating using the interesting punctuation restoration models].

I will like to thank Wanderson M. Pimenta for making their wonderful PyQt GUI interface available publicly without which I would definitely be unable to build up the interface quick enough and Martin Fitzpack for his amazing books on PyQt5 as they serve as excellent reference guides when implementing the transcriber functionality,

Finally, Google and Houndify, because the application makes use of both Google and Houndify speech-to-text API, I discovered that while being two times slower than Google, Houndify tends to be more accurate. Although as a goal, I am aiming to make the application completely offline using deep learning models to power the speech-to-text conversion.

The application is truly amazing! [at least in my perspective], it converts any audio/video to a .flac format, and since Google or Houndify won’t let you dump a 10-hour or a 5-hour video on their API for them to transcribe at once. I had to loop through the audio file to be sending 100sec of the audio to the API, and saving the returned text.

Okay, I fibbed a little unintentionally, the application won’t just transcribe your 10-hour audio/video for you either, it would let you dump it but it won’t transcribe it. You must have to break it into multiple 1-hour audio/videos before it will transcribe it, this is for memory concerns, as audio in .flac format can become extremely large and might crash some systems that don’t have enough memory.

Whew the issues I encountered, if not for Stack Overflow (Praise Be Upon It — PBUI) this application would not be, the most stressful was in trying to compile my scripts to .exe, and that was because of the FFMPEG binary application I was using to convert audio/video files to the standard format. I solved this by having to modify a third-party package I was using to convert the files programmatically.

Although the GUI leave much to be desired, the primary goal is accomplished, and that is what matters! Now I am further tasked with building and hosting a database and also a backup offline database [I will use PostgreSQL, it is the only DBMS I know] that will contain the transcripts and also implement a search functionality that will be able to search the database using keywords/semantic search [Now, I have no idea how to do that!] and also I have been given another 2 weeks.

Hopefully, by the Grace of God, I will post my progress in the next two weeks, Now let me get back to crazily coding and stalking Stack Overflow (PBUI).

Little Background


Let me tell you a little bit about myself. My name is Nkopuruk Samuel E. (AI Prophet). I am a student at the University of Uyo studying Computer Engineering, I am currently in my fifth year of study. I was first introduced to programming in my third (3rd) year, where we were taught Python (I am too ashamed to reveal what programming language we were taught in our second year — Qbasic — it almost killed any desire I had about programming).

It was love at first code, I became so engrossed in it I went beyond and over what our lecturer taught us and read anything and everything I could lay my hands on about python programming, and I became good at it fairly quickly.

In my search for an area of specialization using python, I taught myself web development (Backend) using Django built and uploaded websites from scratch on PythonAnywhere, Desktop development using PyQt5 (developed a Pomodoro Timer app, an Image app and several littles side ones).

I was like the typical wanderer looking for the ideal field to put roots. On a certain day I was researching where to apply for my IT (I was already in my 4th year first semester and we were going for our IT programme the next semester). I had already settled for being a Desktop developer, as I had a certain dislike for Web Development (partly due to most of my friends were going for it) and to its very easy learning curve (I needed something more challenging). I came across an article about Machine Learning (forgotten the name), it classified programming into two styles, traditional (or classical) and Machine learning.

It talked about how classical programmers have to specify every rule in their programs and that any rule not explicitly specified crashes the programs if not explicitly handled by the programmer (and that was something I readily agree to) and how ML programming eliminates that problem by making the machine learn by itself those rules! (I was astounded!)

To cut the long story short, I declined going as an Intern at a company I applied for to learn software development. I spent time searching for where I could intern as a Data Scientist, the ones I could find, their prices were so exorbitant I had to forgo them (as they were likely not looking for student interns, but business executives who wish to add data analyst to their skill set, guessing from the prices they set). So I spent my IT by myself learning and devouring everything I could from textbooks to online courses to YouTube tutorials.

I learned about Data Science Nigeria (DSN) from one of my online searches for some data science communities in Nigeria especially Uyo (no offence DSN you guys virtually have no presence here in the University of Uyo or Akwa Ibom, and I am looking forward to changing that). I highly anticipated this year AI Bootcamp qualifications and I could not wait to join to test my newly acquired ML skills besides my fellow Nigerian ML enthusiasts.

Before I go on, let me tell you a little about my psyche. I tend a little to be a megalomaniac. After my IT, I have this thought that I am among the top 30 most skilful upcoming ML enthusiasts in Nigeria (the top half of 30 mind you) and coming into this competition I was confident of being among the people to compete for the 2 million cash prize. That delusion was continuously fed as I was among the top 20 positions for the first three days. But imagine my surprise as my rank gradually decreased and my name didn’t appear on the list on the first page of the leadership board! And as much as I tried, I could do nothing to change it.

This blow has been an eye-opener for me and it has set a fire in my soul. I want to use this opportunity to thank Data Science Nigeria for giving me this tremendous opportunity to let me become aware that there are freaks out there (students or otherwise) who are better than me at ML and I now know your all names and have written them in a list and nothing will give me greater JOY than to strike your names out of the list as I surpass each and every one of you.

Thank you very much DSN, I now understand the true meaning of the saying “Someone who thinks they know everything, improves at nothing”. Complacency was my biggest obstacle but you have cured me of it by showing me that I am at the bottom of the ladder, and not only that you have set a path for me (not only me) that can enable me get to the top.

I am a computer engineering undergrad and I have been studying machine learning for a cumulative period of one year and I still consider myself a newbie, because anytime I think I have reached the end of the road with a concept, I discovered that there are 10 more roads waiting behind it and so on. Through my relatively short journey, I have gone through quite a few rough patches and have gotten stuck in a few quagmire, I have gotten through them with the help of authors from their books I read, helpful answers from stack overflow and through pure grits. I have gained insights that I think would be helpful to my fellow newbies who are not far behind me.

Passion isn’t everything, just as in the real world, so it is in the programming world. There are challenges that can and will try their possible best to crush the life out of that passion. Most people who go into data science are mesmerized by what can be achieved by it, what has been achieved by it, which leads them to drool at the prospects of what they can achieve by it when they use it.

And as the bible passage says “Faith cometh by hearing, and hearing by the word of God”, their passion grows stronger the more they read articles of amazing projects and developments going on in data science. Yes, they are prepared to study harder, sleep late night and all other things that people who are passionate about something do to reach a competency level, they have never been more passionate about anything else in their life before.

You may have gotten to the stage where you are now purely based on the strength of that passion, if you don’t find something else to support it, it will literally be impossible to go far.

Most newbie never for once consider that their own computer could be their greatest challenge. Data science is the only field of programming where the most emphasis is placed your computer processing capabilities. Other programming field which are near-like instantaneous like Web, Desktop (GUI) and Database development are not overly dependent on your RAM and processor speed, they can easily be developed on a low-end computer just as like on a high-end computer.

When I say low-end computers, those whose computers' have over 16gb RAM and additional GPU card are not included, I only refer to those who use computers with no more than 8GB of RAM and 1GB or less of GPU. Over 80% of developers in Nigeria uses computers with these spec, and with computer of 4GB of RAM and no GPU been the overwhelming majority.

Through my journey in studying Machine learning, my faithful companion is a Dell Windows 10 4GB RAM 2th Generation Corei5 Intel processors with no GPU. The beginning journey was extremely smooth, training on the iris, Boston housing datasets was with near-like instantaneous speed (or close enough to it). It was amazing, all my datasets could fit into memory, any error occurred could be easily be debugged, and most often than not, it could be a syntax error or a dataset not properly formatted, containing values that trips up the machine learning model (as I said easily overcome). But as my expertise increased one particular error became increasingly frequent, the Numpy Out of Memory error. I could no longer run codes written by authors without generating that error.

Obviously, The authors uses computer with a much higher RAM, and I bet, additional GPUs to execute their codes.

The out of memory error became so ubiquitous that we became old friends, it got to the point that whenever I run a code from a textbook and it didn't raise the error, I become suspicious and wonder if I may have made a mistake in implementing the author's code.

This was an almost unsurmountable obstacle for me, to the extent it forces me to give up on projects when their datasets couldn't all be loaded into memory because some machine learning model needed them all at once (e.g non-linear models) or that the dataset could be loaded into memory but there is no space left for the model to train it parameters (which is even worse).

The spirit (your passion) indeed is willing, but the flesh (your computer) is weak. - Matt 26: 41

This error forced me to gain a deeper understanding of memory management, I no longer see data types as oh those, I now say what data type with the minimum memory footprint will best represent my dataset with minimal loss of information. I adopted functional programming paradigm when executing tasks (i.e. I execute instructions in functions and return the needed value(s), leaving python to delete variables arbitrary created during execution when it has run out of scope ) to prevent leaving around unused variables that is hogging up memory.

Thank GOD for Deep learning algorithm who with all their power are not greedy enough to want all the data at once but are always hungry for chunks of them [no matter the size], and for Tensorflow data API and Numpy memmap function who provides the means to feed such chunks.

Believe me, if you persevere, you will become a more tenacious machine learning engineers than your peers who cruise with their huge RAM computer, creating a one-hot encoded arrays with a np.int64 data type, or worse not knowing what data type was used to build that array.

Should you eventually acquire a huge RAM computer, you would still maintain that frugality in assigning RAM resources.

I urge all machine learning beginners to look within themselves carefully and consider if they have the grit needed to forge on when their passion no longer pull them forward, not wasting months instead going on ahead only to then back down when faced with overwhelming obstacle, months that would have been used to learn programming in other fields (like web dev) because they think they can fake it. If you read this and still think you have what it takes, then forge on, see every obstacle as an opportunity to learn some new technical skills. Use tools in ways it wasn't designed to be used, if that still doesn't work, create your own toolset.

God loves you!