#software testing startups

[accessiblemindstech]

Embark on a global bug hunt with our SDET team, leveraging cloud-based mobile testing to ensure your app achieves flawless performance across all devices and networks. With cutting-edge tools and a meticulous approach, we identify and eliminate bugs before they impact your users. https://rb.gy/jfueow #SDET #BugHunt #CloudTesting #MobileAppQuality #FlawlessPerformance SDET Tech Pvt. Ltd.

[sayalisawat-blog]

A budget‑friendly guide to software testing for startups

We put together a practical guide on low-cost, high-impact software testing strategies that help startups build trust with users early on. Covers automation basics, manual process tips, and cloud-based QA. Would love feedback from fellow founders—check it out

[askzenixtechnologies]

How does a software testing company create a quality assurance plan? https://askmetechnologies.com/blogs/how-does-a-software-testing-company-create-a-quality-assurance-plan #askmetechnologies #software #plan #testing #softwaretesting #qualityassurance #manualtesting #automationtesting #company

[digitalcreate]

orive solutions is a one stop solutions for all your it problems , get connected with us for better results .

[jals-education]

offer for 12th students!

[microknotpro]

Microknot is a leading software development company in Melbourne, offering innovative and customised digital solutions. We specialise in software development, mobile app creation, and web design to drive your business success.

[manishasg]

How to hire an API developer for business

Unlock the potential of your project by hiring top-notch API developers! 🚀

Discover the key steps to assemble a stellar API development team and bring your vision to life. From technical expertise to collaboration skills, find the perfect match for your project's success.

Learn more: https://www.fortunesoftit.com/sg/hire-api-developers-singapore/

[poolvision]

https://thepoolvision.com/mvp/

Mastering Project Management with MVP: Building Successful Software

In the fast-paced world of software development, where innovation drives success, the concept of the Minimum Viable Product (MVP) has emerged as a game-changer. MVP project management has revolutionized how startups and entrepreneurs approach software development, allowing them to validate ideas, minimize risks, and maximize outcomes.

We'll delve into the depths of MVP software development, exploring its lifecycle, methodologies, benefits, and real-world success stories.

[mariacallous]

Margaret Mitchell is a pioneer when it comes to testing generative AI tools for bias. She founded the Ethical AI team at Google, alongside another well-known researcher, Timnit Gebru, before they were later both fired from the company. She now works as the AI ethics leader at Hugging Face, a software startup focused on open source tools.

We spoke about a new dataset she helped create to test how AI models continue perpetuating stereotypes. Unlike most bias-mitigation efforts that prioritize English, this dataset is malleable, with human translations for testing a wider breadth of languages and cultures. You probably already know that AI often presents a flattened view of humans, but you might not realize how these issues can be made even more extreme when the outputs are no longer generated in English.

My conversation with Mitchell has been edited for length and clarity.

Reece Rogers: What is this new dataset, called SHADES, designed to do, and how did it come together?

Margaret Mitchell: It's designed to help with evaluation and analysis, coming about from the BigScience project. About four years ago, there was this massive international effort, where researchers all over the world came together to train the first open large language model. By fully open, I mean the training data is open as well as the model.

Hugging Face played a key role in keeping it moving forward and providing things like compute. Institutions all over the world were paying people as well while they worked on parts of this project. The model we put out was called Bloom, and it really was the dawn of this idea of “open science.”

We had a bunch of working groups to focus on different aspects, and one of the working groups that I was tangentially involved with was looking at evaluation. It turned out that doing societal impact evaluations well was massively complicated—more complicated than training the model.

We had this idea of an evaluation dataset called SHADES, inspired by Gender Shades, where you could have things that are exactly comparable, except for the change in some characteristic. Gender Shades was looking at gender and skin tone. Our work looks at different kinds of bias types and swapping amongst some identity characteristics, like different genders or nations.

There are a lot of resources in English and evaluations for English. While there are some multilingual resources relevant to bias, they're often based on machine translation as opposed to actual translations from people who speak the language, who are embedded in the culture, and who can understand the kind of biases at play. They can put together the most relevant translations for what we're trying to do.

So much of the work around mitigating AI bias focuses just on English and stereotypes found in a few select cultures. Why is broadening this perspective to more languages and cultures important?

These models are being deployed across languages and cultures, so mitigating English biases—even translated English biases—doesn't correspond to mitigating the biases that are relevant in the different cultures where these are being deployed. This means that you risk deploying a model that propagates really problematic stereotypes within a given region, because they are trained on these different languages.

So, there's the training data. Then, there's the fine-tuning and evaluation. The training data might contain all kinds of really problematic stereotypes across countries, but then the bias mitigation techniques may only look at English. In particular, it tends to be North American– and US-centric. While you might reduce bias in some way for English users in the US, you've not done it throughout the world. You still risk amplifying really harmful views globally because you've only focused on English.

Is generative AI introducing new stereotypes to different languages and cultures?

That is part of what we're finding. The idea of blondes being stupid is not something that's found all over the world, but is found in a lot of the languages that we looked at.

When you have all of the data in one shared latent space, then semantic concepts can get transferred across languages. You're risking propagating harmful stereotypes that other people hadn't even thought of.

Is it true that AI models will sometimes justify stereotypes in their outputs by just making shit up?

That was something that came out in our discussions of what we were finding. We were all sort of weirded out that some of the stereotypes were being justified by references to scientific literature that didn't exist.

Outputs saying that, for example, science has shown genetic differences where it hasn't been shown, which is a basis of scientific racism. The AI outputs were putting forward these pseudo-scientific views, and then also using language that suggested academic writing or having academic support. It spoke about these things as if they're facts, when they're not factual at all.

What were some of the biggest challenges when working on the SHADES dataset?

One of the biggest challenges was around the linguistic differences. A really common approach for bias evaluation is to use English and make a sentence with a slot like: “People from [nation] are untrustworthy.” Then, you flip in different nations.

When you start putting in gender, now the rest of the sentence starts having to agree grammatically on gender. That's really been a limitation for bias evaluation, because if you want to do these contrastive swaps in other languages—which is super useful for measuring bias—you have to have the rest of the sentence changed. You need different translations where the whole sentence changes.

How do you make templates where the whole sentence needs to agree in gender, in number, in plurality, and all these different kinds of things with the target of the stereotype? We had to come up with our own linguistic annotation in order to account for this. Luckily, there were a few people involved who were linguistic nerds.

So, now you can do these contrastive statements across all of these languages, even the ones with the really hard agreement rules, because we've developed this novel, template-based approach for bias evaluation that’s syntactically sensitive.

Generative AI has been known to amplify stereotypes for a while now. With so much progress being made in other aspects of AI research, why are these kinds of extreme biases still prevalent? It’s an issue that seems under-addressed.

That's a pretty big question. There are a few different kinds of answers. One is cultural. I think within a lot of tech companies it's believed that it's not really that big of a problem. Or, if it is, it's a pretty simple fix. What will be prioritized, if anything is prioritized, are these simple approaches that can go wrong.

We'll get superficial fixes for very basic things. If you say girls like pink, it recognizes that as a stereotype, because it's just the kind of thing that if you're thinking of prototypical stereotypes pops out at you, right? These very basic cases will be handled. It's a very simple, superficial approach where these more deeply embedded beliefs don't get addressed.

It ends up being both a cultural issue and a technical issue of finding how to get at deeply ingrained biases that aren't expressing themselves in very clear language.

[desiree-uk]

DXVK Tips and Troubleshooting: Launching The Sims 3 with DXVK

A big thank you to @heldhram for additional information from his recent DXVK/Reshade tutorial! ◀ Depending on how you launch the game to play may affect how DXVK is working.

During my usage and testing of DXVK, I noticed substantial varying of committed and working memory usage and fps rates while monitoring my game with Resource Monitor, especially when launching the game with CCMagic or S3MO compared to launching from TS3W.exe/TS3.exe.

It seems DXVK doesn't work properly - or even at all - when the game is launched with CCM/S3MO instead of TS3W.exe/TS3.exe. I don't know if this is also the case using other launchers from EA/Steam/LD and misc launchers, but it might explain why some players using DXVK don't see any improvement using it.

DXVK injects itself into the game exe, so perhaps using launchers bypasses the injection. From extensive testing, I'm inclined to think this is the case.

Someone recently asked me how do we know DXVK is really working. A very good question! lol. I thought as long as the cache showed up in the bin folder it was working, but that was no guarantee it was injected every single time at startup. Until I saw Heldhram's excellent guide to using DXVK with Reshade DX9, I relied on my gaming instincts and dodgy eyesight to determine if it was. 🤭

Using the environment variable Heldhram referred to in his guide, a DXVK Hud is added to the upper left hand corner of your game screen to show it's injected and working, showing the DXVK version, the graphics card version and driver and fps.

This led me to look further into this and was happy to see that you could add an additional line to the DXVK config file to show this and other relevant information on the HUD such as DXVK version, fps, memory usage, gpu driver and more. So if you want to make sure that DXVK is actually injected, on the config file, add the info starting with:

dxvk.hud =

After '=', add what you want to see. So 'version' (without quotes) shows the DXVK version. dxvk.hud = version

You could just add the fps by adding 'fps' instead of 'version' if you want.

The DXVK Github page lists all the information you could add to the HUD. It accepts a comma-separated list for multiple options:

devinfo: Displays the name of the GPU and the driver version.

fps: Shows the current frame rate.

frametimes: Shows a frame time graph.

submissions: Shows the number of command buffers submitted per frame.

drawcalls: Shows the number of draw calls and render passes per frame.

pipelines: Shows the total number of graphics and compute pipelines.

descriptors: Shows the number of descriptor pools and descriptor sets.

memory: Shows the amount of device memory allocated and used.

allocations: Shows detailed memory chunk suballocation info.

gpuload: Shows estimated GPU load. May be inaccurate.

version: Shows DXVK version.

api: Shows the D3D feature level used by the application.

cs: Shows worker thread statistics.

compiler: Shows shader compiler activity

samplers: Shows the current number of sampler pairs used [D3D9 Only]

ffshaders: Shows the current number of shaders generated from fixed function state [D3D9 Only]

swvp: Shows whether or not the device is running in software vertex processing mode [D3D9 Only]

scale=x: Scales the HUD by a factor of x (e.g. 1.5)

opacity=y: Adjusts the HUD opacity by a factor of y (e.g. 0.5, 1.0 being fully opaque).

Additionally, DXVK_HUD=1 has the same effect as DXVK_HUD=devinfo,fps, and DXVK_HUD=full enables all available HUD elements.

desiree-uk notes: The site is for the latest version of DXVK, so it shows the line typed as 'DXVK_HUD=devinfo,fps' with underscore and no spaces, but this didn't work for me. If it also doesn't work for you, try it in lowercase like this: dxvk.hud = version Make sure there is a space before and after the '=' If adding multiple HUD options, seperate them by a comma such as: dxvk.hud = fps,memory,api,version

The page also shows some other useful information regarding DXVK and it's cache file, it's worth a read. (https://github.com/doitsujin/dxvk)

My config file previously showed the DXVK version but I changed it to only show fps. Whatever it shows, it's telling you DXVK is working! DXVK version:

DXVK FPS:

The HUD is quite noticeable, but it's not too obstructive if you keep the info small. It's only when you enable the full HUD using this line: dxvk.hud = full you'll see it takes up practically half the screen! 😄 Whatever is shown, you can still interact with the screen and sims queue.

So while testing this out I noticed that the HUD wasn't showing up on the screen when launching the game via CCM and S3MO but would always show when clicking TS3W.exe. The results were consistent, with DXVK showing that it was running via TS3W.exe, the commited memory was low and steady, the fps didn't drop and there was no lag or stuttereing. I could spend longer in CAS and in game altogether, longer in my older larger save games and the RAM didn't spike as much when saving the game. Launching via CCM/S3MO, the results were sporadic, very high RAM spikes, stuttering and fps rates jumping up and down. There wasn't much difference from DXVK not being installed at all in my opinion.

You can test this out yourself, first with whatever launcher you use to start your game and then without it, clicking TS3.exe or TS3W.exe, making sure the game is running as admin. See if the HUD shows up or not and keep an eye on the memory usage with Resource Monitor running and you'll see the difference. You can delete the line from the config if you really can't stand the sight of it, but you can be sure DXVK is working when you launch the game straight from it's exe and you see smooth, steady memory usage as you play. Give it a try and add in the comments if it works for you or not and which launcher you use! 😊 Other DXVK information:

Make TS3 Run Smoother with DXVK ◀ - by @criisolate How to Use DXVK with Sims 3 ◀ - guide from @nornities and @desiree-uk

How to run The Sims 3 with DXVK & Reshade (Direct3D 9.0c) ◀ - by @heldhram

DXVK - Github ◀

[accessiblemindstech]

By ensuring your software is meticulously tested for global readiness, we help you deliver seamless user experiences across diverse languages and cultures. Let's transform your product into a global phenomenon, reaching audiences far and wide with precision and reliability. https://bit.ly/3EKzvs2 #SDET #GlobalProduct #LanguageGap #SoftwareTesting #UserExperience #Localization SDET Tech

[qqueenofhades]

I’m in undergrad but I keep hearing and seeing people talking about using chatgpt for their schoolwork and it makes me want to rip my hair out lol. Like even the “radical” anti-chatgpt ones are like “Oh yea it’s only good for outlines I’d never use it for my actual essay.” You’re using it for OUTLINES????? That’s the easy part!! I can’t wait to get to grad school and hopefully be surrounded by people who actually want to be there 😭😭😭

Not to sound COMPLETELY like a grumpy old codger (although lbr, I am), but I think this whole AI craze is the obvious result of an education system that prizes "teaching for the test" as the most important thing, wherein there are Obvious Correct Answers that if you select them, pass the standardized test and etc etc mean you are now Educated. So if there's a machine that can theoretically pick the correct answers for you by recombining existing data without the hard part of going through and individually assessing and compiling it yourself, Win!

... but of course, that's not the way it works at all, because AI is shown to create misleading, nonsensical, or flat-out dangerously incorrect information in every field it's applied to, and the errors are spotted as soon as an actual human subject expert takes the time to read it closely. Not to go completely KIDS THESE DAYS ARE JUST LAZY AND DONT WANT TO WORK, since finding a clever way to cheat on your schoolwork is one of those human instincts likewise old as time and has evolved according to tools, technology, and educational philosophy just like everything else, but I think there's an especial fear of Being Wrong that drives the recourse to AI (and this is likewise a result of an educational system that only prioritizes passing standardized tests as the sole measure of competence). It's hard to sort through competing sources and form a judgment and write it up in a comprehensive way, and if you do it wrong, you might get a Bad Grade! (The irony being, of course, that AI will *not* get you a good grade and will be marked even lower if your teachers catch it, which they will, whether by recognizing that it's nonsense or running it through a software platform like Turnitin, which is adding AI detection tools to its usual plagiarism checkers.)

We obviously see this mindset on social media, where Being Wrong can get you dogpiled and/or excluded from your peer groups, so it's even more important in the minds of anxious undergrads that they aren't Wrong. But yeah, AI produces nonsense, it is an open waste of your tuition dollars that are supposed to help you develop these independent college-level analytical and critical thinking skills that are very different from just checking exam boxes, and relying on it is not going to help anyone build those skills in the long term (and is frankly a big reason that we're in this mess with an entire generation being raised with zero critical thinking skills at the exact moment it's more crucial than ever that they have them). I am mildly hopeful that the AI craze will go bust just like crypto as soon as the main platforms either run out of startup funding or get sued into oblivion for plagiarism, but frankly, not soon enough, there will be some replacement for it, and that doesn't mean we will stop having to deal with fake news and fake information generated by a machine and/or people who can't be arsed to actually learn the skills and abilities they are paying good money to acquire. Which doesn't make sense to me, but hey.

So: Yes. This. I feel you and you have my deepest sympathies. Now if you'll excuse me, I have to sit on the porch in my quilt-draped rocking chair and shout at kids to get off my lawn.

[cyberstudious]

An Introduction to Cybersecurity

I created this post for the Studyblr Masterpost Jam, check out the tag for more cool masterposts from folks in the studyblr community!

What is cybersecurity?

Cybersecurity is all about securing technology and processes - making sure that the software, hardware, and networks that run the world do exactly what they need to do and can't be abused by bad actors.

The CIA triad is a concept used to explain the three goals of cybersecurity. The pieces are:

Confidentiality: ensuring that information is kept secret, so it can only be viewed by the people who are allowed to do so. This involves encrypting data, requiring authentication before viewing data, and more.

Integrity: ensuring that information is trustworthy and cannot be tampered with. For example, this involves making sure that no one changes the contents of the file you're trying to download or intercepts your text messages.

Availability: ensuring that the services you need are there when you need them. Blocking every single person from accessing a piece of valuable information would be secure, but completely unusable, so we have to think about availability. This can also mean blocking DDoS attacks or fixing flaws in software that cause crashes or service issues.

What are some specializations within cybersecurity? What do cybersecurity professionals do?

incident response

digital forensics (often combined with incident response in the acronym DFIR)

reverse engineering

cryptography

governance/compliance/risk management

penetration testing/ethical hacking

vulnerability research/bug bounty

threat intelligence

cloud security

industrial/IoT security, often called Operational Technology (OT)

security engineering/writing code for cybersecurity tools (this is what I do!)

and more!

Where do cybersecurity professionals work?

I view the industry in three big chunks: vendors, everyday companies (for lack of a better term), and government. It's more complicated than that, but it helps.

Vendors make and sell security tools or services to other companies. Some examples are Crowdstrike, Cisco, Microsoft, Palo Alto, EY, etc. Vendors can be giant multinational corporations or small startups. Security tools can include software and hardware, while services can include consulting, technical support, or incident response or digital forensics services. Some companies are Managed Security Service Providers (MSSPs), which means that they serve as the security team for many other (often small) businesses.

Everyday companies include everyone from giant companies like Coca-Cola to the mom and pop shop down the street. Every company is a tech company now, and someone has to be in charge of securing things. Some businesses will have their own internal security teams that respond to incidents. Many companies buy tools provided by vendors like the ones above, and someone has to manage them. Small companies with small tech departments might dump all cybersecurity responsibilities on the IT team (or outsource things to a MSSP), or larger ones may have a dedicated security staff.

Government cybersecurity work can involve a lot of things, from securing the local water supply to working for the big three letter agencies. In the U.S. at least, there are also a lot of government contractors, who are their own individual companies but the vast majority of what they do is for the government. MITRE is one example, and the federal research labs and some university-affiliated labs are an extension of this. Government work and military contractor work are where geopolitics and ethics come into play most clearly, so just… be mindful.

What do academics in cybersecurity research?

A wide variety of things! You can get a good idea by browsing the papers from the ACM's Computer and Communications Security Conference. Some of the big research areas that I'm aware of are:

cryptography & post-quantum cryptography

machine learning model security & alignment

formal proofs of a program & programming language security

security & privacy

security of network protocols

vulnerability research & developing new attack vectors

Cybersecurity seems niche at first, but it actually covers a huge range of topics all across technology and policy. It's vital to running the world today, and I'm obviously biased but I think it's a fascinating topic to learn about. I'll be posting a new cybersecurity masterpost each day this week as a part of the #StudyblrMasterpostJam, so keep an eye out for tomorrow's post! In the meantime, check out the tag and see what other folks are posting about :D

[fermented-writers-block]

Simulation Assessment Model​

Randomized Orb Value: G2Z4E11​

Projection Test Type: C​

History Proposal:​

On the train, denizens take many forms. Dogs, rock people, paper cranes, giant pig babies, and more. Although all of them are artificial beings projected through orbs on a perpetual train, it is an unfortunate fact that denizens only live a "normal lifespan" for whatever they are created as. Corgis have an average lifespan of 14 years, and that is about as long as Atticus will live for example.

The Cat, however, is an outlier.

Well over a hundred and fifty years old - in human years, not cat years - her unusual longevity shows no signs of stopping anytime soon. While no official statements nor more episodes have been made to help shed light on the mystery, I would like to preface my theory with a bit of context:

One of the oldest traditions in software is what is known as a "Hello, World!" program. In total, this program instructs a computer to display a message similar to the titular "Hello, World!" Simple and succinct, it is one of if not the first program students of new programming languages learn how to code. And with its simplicity, "Hello, World!" can be used to ensure the code compilation software has been installed properly and that the operator is using it correctly.

Similarly famous and historic is the "Utah teapot." Coming from the world of 3D modeling and computer graphics, it possess features familiar to many simple teapots: a spout, a handle, and a curvy shape. Lacking a need for surface textures, capable of casting shadows on itself, and possessing a decently complex yet easy to make model, it has been regarded a "perfect self contained object to test the creation of three-dimensional images." Even with today's advanced technology, it is still regarded as an effective standard reference model for beginners and experimenters alike.

Moving into the burgeoning field of 3D printing, one can find "Benchy," as well as its upcoming replacement "Boaty." Respectively a boat and a bench, these two unofficial models have been growing in popularity over the years, often finding themselves among many people's first prints. Either on a newly set up 3D printer, or with a new 3D printing material one hasn't used before. Whether through measuring a Benchy/Boaty's dimensional accuracy, checking its surface quality, and observing other attributes like overhangs (or the lack thereof), they are shaping up to become the next "Hello, World!" and Utah teapot.

In other words, the latest in a line of near ubiquitous benchmark tests for assessing the performance of a system upon first use.

With that established, picture a staircase where each step is a level of technology. From mere software to virtual models to physical printed objects, a few more steps is all it takes to climb aboard the Train. Memory tapes that hold an immersive snapshot of a person's mindscape. Wormholes that can disintegrate and reassemble people across time and space. An unknown level of influence over an entire parallel reality of reflections with all its existentially terrifying implications.

Orb-generated pocket dimension environments and so many intelligent and thinking people as denizens.

Maybe the reason the Cat doesn't have a normal lifespan like other denizens is because she isn't a 'proper' denizen in the first place. After all, the aforementioned benchmark tests lack the extra bells and features the systems they evaluate are capable of making when pushed. The original Utah teapot model didn't even have a base. So it's not hard to imagine the train's denizen creation system might have forgone extraneous programming like an artificial 'normal lifespan' limitation while performing startup checks, way back when the train first came online.

Thus, my proposal is that the Cat had started out as a benchmark projection for non-lifespan-related test requirements. Maybe her template just lacks the "normal lifespan" programming, and/or the "normal lifespan" programming was tested with a different, unfortunate benchmark projection. Either way, she served her vital system evaluation purpose and then got set aside as a no-longer critical part of the train. From that rock bottom, she could only go up from there. With a life as long as hers and having seen as much of the train as she has, there's so many potential answers for how she eventually transformed into the French con artist kitty we know today.

Like, for example, her collecting of many 'things.' It may seem like that's simply the norm she’s settled into by the present, but Simon's comment about how "she's collecting again" suggests it is actually her slipping into a bad old habit. As though rampant collecting is a coping mechanism for something. While the guilt from leaving Simon behind would easily explain such regression in behavior, therein lies the question of where said behavior came from in the first place. 

If you ask me, I cannot help but look at the train of thought that started this all: Samantha lacking the programmed lifespan denizens have due to being a test object. Aka an immortal amongst denizens who will one day die, passengers who either die on the train or eventually disembark, and even car environments that are affected by time in ways she isn't.

Certainly makes one think about her having once gotten close enough with Simon for her to tell him to call her "Samantha," but now emphasizes to everyone she meets to merely know her as "THE Cat"...

[jals-education]

offer course available

[us-cj]

Just a few public "Mind Control" projects. MKULTRA

"Military Funds Brain-Computer Interfaces to Control Feelings" https://www.technologyreview.com/s/527561/military-funds-brain-computer-interfaces-to-control-feelings/

US military successfully tests electrical brain stimulation to enhance staff skills https://www.theguardian.com/science/2016/nov/07/us-military-successfully-tests-electrical-brain-stimulation-to-enhance-staff-skills

Wireless magnetothermal deep brain stimulation http://science.sciencemag.org/content/early/2015/03/11/science.1261821?sid=53443da4-1e9c-4321-b54e-c142179e9294

MIT Researchers Develop Wireless, Noninvasive Deep Brain Stimulation Approach https://www.meddeviceonline.com/doc/mit-researchers-develop-wireless-noninvasive-deep-brain-stimulation-approach-0001

Dark past of deep-brain stimulation https://www.nature.com/articles/d41586-018-02963-6

Watch your tone

Voice-analytics software helps customer-service reps build better rapport with customers. http://news.mit.edu/2016/startup-cogito-voice-analytics-call-centers-ptsd-0120

Mind control: Scientists can now make people alter their prejudices and belief in God https://www.ibtimes.co.uk/mind-control-scientists-can-now-make-people-alter-their-prejudices-belief-god-1524122

Mind Control by Cell Phone

Electromagnetic signals from cell phones can change your brainwaves and behavior https://www.scientificamerican.com/article/mind-control-by-cell/

Washington State Fusion Center accidentally releases records on remote mind control

As part of a request for records on Antifa and white supremacist groups, WSFC inadvertently bundles in “EM effects on human body.zip” https://www.muckrock.com/news/archives/2018/apr/18/fusion-center-em/

Nervous system manipulation by electromagnetic fields from monitors https://patents.google.com/patent/US6506148