ACM JU Magazine | 13th Issue

Magazine’s director note

With the rapid development of technology, many new concepts keep popping out. Our goal is to keep up with these concepts and share knowledge.

Students make this magazine from A to Z, that is why I am in love with this magazine. Students who have responsibilities and study to finish put their time into reading, writing, editing, and designing to serve our noble goal.

With this issue of ACM magazine, you will notice various articles in content. We are delighted to share this magazine issue with you.

Your feedback is appreciated. Happy Reading!

Editor in-cheif note

While sad and often scary, change is inevitable. But with change comes the opportunity for growth and development.

As a team made up of students, it is expected for memebers to come and go. It is sad to have friends leave, but we wish them nothing but success in their journeys. We welcome many new authors, editors, and designers along with the growth we hope they bring to the magazine.

Starting with this issue, most of the team now consists of new members, marking a new and exciting chapter for the magazine. It was a pleasure encountering fresh writing styles and working on them with their authors.

Keeping the momentum of our last issue, we gave the authors creative freedom and had them pick topics they would like to write about. We hope you have a great time reading, and for any feedback, our E-mail address: acmju.studentschapter@gmail.com

ACM JU MAGAZINE

Magazine Director Basel Husam

Editor-in-cheif Zain Mohammed

Magazine Designer Basel Anaya

Magazine Designer Jumana Abu Ghoush

Editorial Assistant Shatha Saleh

Editorial Assistant Hassan Al Safadi

Contributing Writer Dunia Otoum

Contributing Writer Aseel Al Zubaidi

Contributing Writer Aseel Al Sahsah

Contributing Writer Basel Husam

EDITORIAL OFFICE

King Abdullah school of Information Technology - 1st floor

| acmju.studentschapter@gmail.com ju.acm.org

ACM JU MAGAZINE

SCIENCE + TECHNOLOGY + ENGINEERING

Contributing Writer Sadeel Suliman

Contributing Writer Saif Shalan

Contributing Writer Sara Al-Mahrouq

Contributing Writer Abedalaziz Alhusni

Contributing Writer Zaid Abu Ghoush

Contributing Writer Maha Anwar

Guest Writer Prof. Ibrahim Al Jarrah ACM JU Treasurer Ammar Abushukur ACM JU Vice President Shatha Thiab ACM JU Chairman Othman Abu-Roqaia

EDITORIAL OFFICE

King Abdullah school of Information Technology - 1st floor 00962781491920 | acmju.studentschapter@gmail.com ju.acm.org

Big Data

The Main Driver of the Fourth Industrial Revolution

By: Prof. Ibrahim Aljarah

Talking about the Fourth Industrial Revolution, also known as Industry 4.0, brings up one of the pop-up questions that come to mind: what is the role and importance of big data that make it a vital technology in the future?

Let us first learn about the term "Big Data" and what elementary characteristics must be available to call the data big data. Does it depend on the data size, or should other features be taken into account?

Simply, describing data as big data is a relative description, meaning that the data is big based on the available resources for storage or processing. For example, if we have data of size 10GB in a specific form, and the storage or processing tools cannot deal with this quantity or quality of data, then we can say that this data is big. All in comparison to the resources and capabilities available for storage or processing. "Data that is considered big for one organization is not necessarily big for another." It is also possible to define big data as data that exceed a petabyte or exabyte. Others define big data as an approach that combines different techniques to extract valuable information from data that does not appear significant. Big data has evolved in the last decade, and its applications have entered the medical and industrial fields, financial markets, government sectors, and many more.

Essential Characteristics of Big Data (Vs)

The Volume:

It is the volume of data extracted from a source. It determines the value and capabilities of the data classified as big data. The size is usually in petabytes or exabytes.

The Variety:

It means the diversity of the extracted data, which helps users to choose the appropriate data for their field of research and includes structured data in databases. As well as unstructured data in the form of images, videos, and short messages, but this type needs the effort to prepare in an appropriate form for processing and analysis.

The Velocity:

It means the speed of data production and extraction to cover the demand. Now, a quick pace is one of the essential elements of de cision-making based on data; the time it takes from when the data enters to the second the decision is made.

The fourth dimension of big data is usually added depending on the context; it can be value, veracity, or valence. The value property means that big data analysis must produce a specific value. Veracity indicates the accuracy and reliability of the data. Valence refers to the ability of big data to form connections with other types of data.

Big Data Management:

Build a big data strategy, meaning a business plan or policy strategy designed to achieve a goal. The strategy consists of four main parts: objective, policy, plan, and action. A big data strategy begins with big goals, not just data aggregation. It's important to integrate big data analytics with business goals, discuss goals and provide buy-in to analytics, build diverse teams and establish a teamwork mindset, access, and share data, and replicate these activities to respond to business goals. Define policies to avoid concerns about using big data in the long run. Such as: what are privacy concerns; who should be able to access and control the data; what is the data age commonly defined as the volatility or segmentation of big data; how the data is coordinated and organized; what ensures long-term data quality; how do different departments within an organization communicate or work with data; and are there any existing legal or regulatory standards?

Tools Used in Big Data Analysis:

We have tools such as Hadoop, Storm, Cloudera Distribution, Amazon EMR, KAFKA, and many others.

What is E2EE

End-to-end encryption is an asymmetric cryptography implementation and is considered the most secure method of communication. Messages are encrypted with an encryption key that can only be decrypted by the device to which they were transmitted. Even the server that is sending the data can't see the message content. No one monitoring the network, including hackers, government agencies, and even service providers, can read the transmitted data.

How E2EE works

So, let's say you send some message to your friend from your phone using any app that has encryption enabled for these two devices to communicate with each other they need an intermediate server in this case that would be a WhatsApp server let's give these devices names so it is easier to reference them both Alice and Bob have two encryption keys one is public encryption key another one is a private encryption key.

By: Aseel Alzubaidi

If Alice sends any message to Bob she uses Bob's public encryption key to encrypt the message and then the message goes to the server you might think that the server may be able to see your message but that is not the case and in that case that wouldn't be called end-to-end encryption anyway because only Bob has the private key it should be used to decrypt the message so the server is just here as an intermediate which makes it possible for these devices to communicate so when Bob receives this message it decrypts the message using the private key and shows it on your screen.

Similarly, if Bob wants to send a message to Alice, he uses her public key when he receives it and decrypts it using a private key, making it impossible for anyone to read your messages while they're on the way.

Why is E2EE important

Personal privacy here is respected, and customer data is kept safe from unauthorized access. Data integrity is achieved as well since unauthorized users do not have the appropriate key to access data while it is in transit. The message sent here is indecipherable for those who come across it deliberately or accidentally achieving confidentiality. Hackers must perform device-level hacks, which are much more complex and time-consuming.

The Year of the Leaks

Knowledge Is Power. Age-old wisdom told us so throughout history. This everlasting statement stands the test of time as information and data rule the world today. The importance of data cannot be understated. Thus it stands to reason that protecting it should be priority number one because it’d be a catastrophe if the data leaks.

2022 has seen this nightmare become real, courtesy of a hacking group that goes by the name of “LAPSUS$.”

The now infamous group managed to bring some of the biggest companies down to their knees as industry pioneers, including but not limited to: Microsoft, Samsung, Uber, and Rockstar, awoke to enormous data breaches and leaks that threatened their existence. But how is it possible that the companies we once thought impenetrable got hacked to a severe degree and in quick succession?

The Weakest Link

By Saif Shalan

Tools of the Trade.

What makes these attacks stand out is the group’s unusual methods of gaining access: instead of employing malware, ransomware, and other typical hacking techniques to target systems, the group targets individuals using social engineering, defined as manipulating and tricking people into giving away confidential information and credentials.

Social engineering has many methods. Firstly, the group acquires the logging credentials that can happen through a variety of techniques: recruitment, where they advertise that they’re willing to buy the credentials from an employee of the company, or “SIM-swapping”: an elaborate scam that starts with “phishing” which is a social engineering technique of its own in which the attacker sends fake emails to trick the victim into divulging their confidential information, and ends with the victim’s phone losing connection to the network, and the attacker receiving the SMS instead.

The group then uses the acquired credentials to access the organization’s VPN or use remote desktop software, and from here on, it is smooth sailing.

The Aftermath

The hacker group managed to get its hands on hundreds of gigabytes of private company data, not to mention the source code of some companies like Rockstar, which is the lifeblood of such companies.

The FBI’s and the police in the UK’s joint efforts came to fruition, as they identified and arrested several members of the group.

Yet it is this writer’s opinion that the damage is irreversible, and these harrowing incidents should serve as a source of reflection, maybe even a wake-up call for these big companies to get to the bottom of why is it that their employees are easy to manipulate, and what does that say about the company’s rules and policies, and its treatment of its employees?

Have you ever been talking to a friend or family member about a specific plan (such as wanting to buy a new pair of glasses), you open your phone, and several ads pop up out of the blue to suggest eyeglasses stores with a lot of discounts?

If your answer is YES, you may feel that your phone is hearing you. Well, data analysts confirm that it's all about something called predictive analysis.

Experts blend predictive analysis with machine learning and AI techniques, which is considered one of the many ways to predict future outcomes and improve the e-commerce experience.

What helps advertisers to reach better decisions are the gathered handy insights, such as your search history, web behaviors, past purchases (and the details that led to them), cookies you accept, your interactions in applications, the likes, comments, saved in-app stuff, and so on.

In addition, predictive intelligence technology makes the power to deliver what online shoppers need even before they dig for the product achievable. With all collected data, companies can specify suitable product recommendations for each individual. They use all this big data to make a huge circle of information about you and build a picture of your wants, needs, and routines which allows ads to target you so well that it seems like they are reading your mind.

Anyway, isn't it a waste of time to record and listen to every word we say? It's the least efficient way for companies to gather any research. If they do, they must collect millions of audio clips, convert them into text, then use those data to customize ads. Not to mention that this process can take longer than hours to take effect.

The actual reason you get an ad for something you said could be you without realizing it. You might have stopped a moment to look at a post of eyeglasses for a little longer on Facebook or passed by a glasses store and stopped there while your location services were active on Google Maps, etc.

To wrap up, the answer to the question "is my phone hearing" is simply no. Because you have already given advertisers all the required information, they don't need to listen to your conversations to suggest the right ad at the perfect moment. The only way to listen is via voice-activated assistants.

Top 10 Mistakes of Entrepreneurs

By Abedalaziz Alhusni

Guy Kawasaki has a BA in psychology from Stanford and MBA from UCLA. Guy worked for Apple in the Macintosh division from 1983 till 1987. He was responsible for convincing people to write mac software, earning him the title “The apple software evangelist.” Guy worked as an advisor for Google and Motorola. With over 30 years of experience in this field, he gathered a list of the top 10 mistakes of entrepreneurs.

#1

Multiplying big numbers by 1%

Taking a big market and thinking the startup will at least get 1 percent of it in the worst case is a mistake. For example, if a startup wants to sell cat food, every can has a return of 3 pounds in profit. In the UK, there are approximately 10.8 million cat owners; 1 percent of that is 108,000 thousand cat owners. The startup might think they would at least sell cat food to 1 percent of the market, earning them a profit of 324,000 pounds daily. The first problem here is getting 1 percent of any market is not as easy as it seems. The second problem is investors don’t want to invest in a startup whose target is 1 percent of the market.

#2 Scaling too soon

The startup decided to rent out warehouses to store their products, and hired workers, managers, developers for their website, and market analysts. If the product ships and no one is interested, the startup will lose money, time, and resources and probably fail.

#3 Partnership

The only thing that counts as a success in a business is sales. Partnering is just two or more businesses working together to cover each other’s weaknesses to gain sales. The partnership has no real value.

#4 Pitching instead of prototyping

Many startups focus on the pitching pro cess and forget about prototyping. That is a mistake; investors would rather see a working prototype than a great pitch.

#5 Poor presentation

A crowded PowerPoint presentation with lots of text and small font sizes is a mistake. The presentation should be simple, not exceeding 20 minutes, and has ten slides with 30-point fonts.

#6 Doing things sequentially

Do not do things one by one. A startup requires the entrepreneur to do multiple things at the same time. For example, the entrepreneur might have to deal with investors, hiring, and selling all at once.

#7 51% means control

Some entrepreneurs believe owning 51% of the company means they control it. That is an illusion; Guy never encountered a situation where the vote was won by 51% of shareholders. The moment investors invest in a startup, the entrepreneur loses control of the company.

#8 Believing patents mean protection

In the real world, patents will not help you. The only scenario where patents are essential is when getting acquired by another company. Startups cannot deal with lawsuits, especially against large companies.

#9

Hiring the same kind of people

When this happens, there are fundamental weaknesses in the startup. A startup should balance its workforce between making, selling, and collecting the product.

#10

Befriending investors

Investors are in the business to make money, not to make friends.

Ar A bic N A tur A l lAN gu A ge Processi N g: Problems AN d Pote N ti A l

By Zaid Abu Ghoush

Have you ever used Siri, Cortana, Google Translate, or Grammarly? If you have, you have used technology powered by natural language processing (NLP). NLP is a field that stands at the intersection of linguistics and artificial intelligence (AI). It is concerned with studying and developing algorithms and tools that enable a computer to understand human language in both written and spoken form and even generate it.

NLP dates back to the 1950s, before the advent of machine learning. Back then, programs were based on complex hand-coded rules and were limited in their applications. Machine learning caused a revolution in NLP that catapulted it to where it is today.

When it comes to NLP, the list of applications is long. It is the driving technology behind speech recognition, text-proofing, chatbots, text summarization tools, sentiment analysis, and optical character recognition. It also has some exotic applications like text-to-image generation, most famously used in a program called DALL-E 2. This program can generate images solely based on text input given by the user!

Unfortunately, the incredible progress made in NLP is limited to a few languages, namely English. Arabic has lagged considerably behind despite being spoken by over 400 million people globally.

The most obvious reason is that there is not one type of Arabic. We have Modern Standard Arabic, or “Fusha,” used in literature and mass media. We also have over 25 Arabic dialects in daily communication and informal settings!

Furthermore, the Arabic language is rich morphologically. The same verb could have a large number of derivations! In addition, Arabic also has no vowels. Instead, we use diacritics: marks placed above or below the letter. Unfortunately, we rarely write those diacritics, so they are largely absent from the datasets machines use to train.

All these reasons, and others, contribute to data sparsity. There is a limited number of Arabic datasets, especially in different Arabic dialects, which are central to a human-like feeling when interacting with a computer. Luckily, more and more people are recognizing the importance of developing the field of Arabic NLP. The technology Innovation Institute in Abu Dhabi launched NOOR, the world’s most powerful Arabic language model to date. There are now multiple dedicated NLP research labs, such as the CAMeL lab, also based in Abu Dhabi. More and more companies specializing in providing NLP services have begun to pop up, such as Xina and Labiba here in Jordan.

Admittedly, there is a long way to go, but with more hands on deck, we might catch up sooner than later, and who knows, maybe we will hear Siri speak in a Jordanian dialect one day!

With the revolution of deep learning, it is now possible for computers to "see" and learn like humans. This technology is called Computer Vision.

Computer vision is a form of artificial intelligence that lets computers identify things using algorithms trained to collect predefined features helping them pick objects out of the crowd.

Social networking sites use computer vision to automatically identify people and tag images they upload. Companies also use computer vision for self-driving cars and many other applications.

Examples of Computer Vision

1. Image Classification is the process by which computers determine what type of object an image contains.

2. Object Detection is the process by which a computer identifies where objects in an image are located.

3. Object Recognition is the process of identifying an object by its shape and or size in an image or video frame.

4. Face Recognition is the process of identifying a person from a photograph or video frame based on their facial features such as their face shape, skin tone, eye color, and facial hair.

CNN (Convolutional Neural Network)

CNN is a network of interconnected nodes, each consisting of a set of filters that apply specific mathematical transformations to the input image.

CNN helps the deep learning model see by breaking the images down into pixels, edges, then whole objects. After that, it can recognize what the object is.

Need More Data

Unfortunately, large datasets that include a large number of labeled examples are needed for the training of deep neural networks.

For example, binary image classification between humans and horses needs enormous amounts of data; sometimes, 75K is not enough. That is the reason why we need data augmentation to increase the amount of data.

Data Augmentation is a technique to increase the number of images using different techniques such as rotation, cropping, rescaling, shifting, etc.

Applications of Computer Vision

1. Self-driving cars can view their surroundings to react to potential dangers.

2. Many types of cameras, including security cameras, rely on computer vision to perform image analysis, detecting faces, animals, traffic signs, etc.

3. Doctors are applying computer vision techniques to diagnose and treat disease through medical imaging like CT scans, MRIs, and X-rays.

One Galaxy, An Entire Universe

By Dunia Alatoom

By making extensive observations of the universe, cosmologists can often ascertain its composition. However, these researchers have discovered that a machine learning algorithm can examine a single virtual galaxy and make general predictions about the structure of the virtual universe in which it exists. The robots appear to have discovered a pattern that could one day enable astronomers to create generalizations about the true cosmos only by looking at its fundamental components.

The extraordinary discovery resulted from an assignment Villaescusa-Navarro gave to Jupiter Ding to create a neural network that can estimate a few cosmological features based on the characteristics of a galaxy. The purpose was to introduce Ding to machine learning. Then they realized the algorithm had accurately predicted the overall density of the material. Researchers examined two thousand virtual universes produced by the Cosmology and Astrophysics using Machine Learning Simulations (CAMELS) project. These universes are composed of 10% to 50% matter to 90% dark energy, which causes the cosmos to expand at an accelerating rate. Galaxies formed as dark matter and visible matter merged during the simulations. A rough representation of complex events like supernovas and the jets that come from supermassive black holes was included in the simulations. Within these several virtual universes, Ding’s neural network examined about a million simulated galaxies. It was sensible of each galaxy’s size, composition, mass, and more than a dozen other properties. It attempted to draw a connection between this collection of statistics and the parent universe’s matter density.

The neural network was tested on thousands of new galaxies from hundreds of undiscovered universes and succeeded in predicting the cosmic density of matter to within 10%.

dozen other properties. It attempted to draw a connection between this collection of statistics and the parent universe’s matter density.

The team worked for six months to explore how the neural network had become so intelligent. They ensured the algorithm hadn’t simply discovered a way to get the density from the simulation’s code rather than from the galaxies themselves. The researchers conducted several studies to understand how the algorithm determined cosmic density. They focused on the most crucial characteristics by continuously retraining the network while systematically hiding certain cosmic aspects.

“You would anticipate galaxies to get heavier and spin more quickly in a universe filled with dark matter. Therefore, you might assume that rotation speed and cosmic matter density are related. However, this assumption is based on an unreliable correlation,” said Volker Springel.

The neural network discovered a far more complex and accurate association between the matter density and 17 or so galaxy features. Despite galaxy mergers, star explosions, and black hole eruptions, this bond endures.

Countess Lovelace

“Your best and wisest refuge from all troubles is in your science.” Augusta Ada King Byron, Countess of Lovelace. She was born on December 10th, 1815, in Piccadilly Terrace, Middlesex (now in London), and died on November 27th,1852, in Marylebone, London. An English mathematician is known to be the first computer programmer and an associate of Charles Babbage, an English mathematician and the first to attempt but has only developed plans for the Analytical Engine alongside Ada’s notation.

Early life and Education

Ada Byron was the daughter of Lord Byron, the famed poet, and his wife Annabella, whom he left and the entire country two months after Ada was born. Ada’s mother paid the most attention to her tuition by getting her private tutors and encouraging her to study by herself. When she reached the point of advanced studies, she received help from the mathematician-logician Augustus De Morgan, the first mathematics professor at the University of London. When she turned 19, she married William King the 8th Baron, who became an earl, and by association, she became a countess of Lovelace.

Career

Ada became an acquaintance of Charles Babbage when she met him through a mutual friend, author Mary Somerville when she was 18. This relationship grew when she translated and notated an article written by Italian mathematician Luigi Menabrea, “Notions Sur la machine analytique de Charles Babbage,” which translates to “Elements of Charles Babbage’s Analytical Machine.” Her notes and descriptions were an excellent fit for the topic itself; one of her noticeable descriptions was what she said about the Analytical Engine: “The Analytical Engine weaves algebraic patterns, just as the Jacquard loom weaves flowers and leaves.” The Analytical Engine was never fully built. However, Ada’s endeavors were appreciated and called to mind.

Facts worth stating

1. The 1980 Ada programming language was named after her.

2. Ada Lovelace’s day is the second Tuesday in October.

3. She wrote the first published computer program.

4. Her mother was scared stiff that she would turn out like her father, a mad romantic, yet she amalgamated the arts and science into "poetical science."

5. She died young of uterine cancer in the 1850s and was buried next to her father in St Mary Magdalene Church in Hucknall, Nottinghamshire.

Are The Movies Tricking Us Or Is it Our Brain?

By: Sadeel Suliman

Our brains have been evolving for more than 4 billion years, so how can a flicker on the screen immerse us into feeling reality about imaginary scenes? Why do you cry, scream, or laugh when watching a movie?

The films' power to immerse us is based on two factors, The neurological factor and the psychological factor

The psychological factor relies on fundamental human characteristics developed over centuries of social interaction and shared experience and depends on two rules.

The mirror rule presents our subconscious desire to mimic the same thing as people around us do. It explains why we often copy the behavior of others around us and why, whether in real life or on-screen during a movie, we react with emotion when we see someone else laugh or cry.

The success rule is "Repeat what has worked before." When flying objects approach: we've learned to duck. When facing danger, we are prepared to fight or run. When we see a movie, we adopt the same behaviors.

The neurological factor is more connected to our senses.

In one fMRI study, moviegoers observed simultaneous increases and decreases in brain activity. Up to 70% of their cerebral cortex is synchronized at any given time. This synchronization occurred mainly in brain areas involved in processing sights and sounds but was also observed in emotional brain regions.

Movie cuts and angle modifications have significance on viewers' eye movements, creating dramatic disruption to the visual information obtained by retinal cells as it travels to the visual cortex in the brain. Visual cortex regions perform functions such as pattern identification and motion perception.

As a result, films with more control over their audiences' perceptions have a powerful influence on their brain activity.

When we watch horror movies—even though we know it's not real—adrenaline is released. And we find ourselves on the edge of our seats because the stimulus is so powerful that it bypasses our calm state and taps into an instinctual need to act quickly to defend ourselves before we recognize what startled us. However, the reason that causes us to resonate and connect to fictional characters is the endorphin hormone, the "empathy hormone."

"Our brains didn't evolve to watch movies. Movies evolved to take advantage of the brains we have," said neuroscientist Jeffrey M. Zacks. Science cannot replace artistry, but it should help us understand why we make some decisions. After all, The best films can always help us reveal something new about our brains.

But why do different movie genres make us experience various emotions?

Inside Amazon’s Smart Warehouse

By Sara Al-Mahrouq

Amazon posted some Latest technologies designed to improve the safety of Amazon employees, which outlined four different robotic systems developed by Amazon’s Robotics and Advanced Tech teams. Three of these systems are mobile robots that have significantly expanded the warehouse storage space over the past decade. Amazon was among the first e-commerce businesses to recognize the power of warehouse robots.

Let’s take a look at three of the mobile robots presented by Amazon in their blog post: Proteus

Proteus is Amazon’s first ‘fully autonomous’ mobile warehouse robot. Safely integrating robotics into the same workspace as humans used to be difficult, but Proteus is changing that while remaining clever, safe, and collaborative.

Proteus can lift and move items, a non-automated, wheeled transport used for transporting packages across its facilities, while safely navigating around the company’s employees. The robots’ design is to be self-directed and move around employees, eliminating the need for restriction to specific areas.

Amazon aims to automate GoCart procedure handling across the network, limiting the need for people to manually move heavy objects throughout its facility and freeing them to spend their time on more rewarding work.

Cardinal

The movement of heavily loaded packages, with the reduction of twisting and turning motions by workers, is placed in which automation is constantly being looked at to decrease the risk of injury. Cardinal is a robotic work cell that chooses one package from a stack, lifts it, reads the label, and accurately places it on the shelves to send the package on its way. Cardinal reduces the chance of workplace injuries by performing tasks that require lifting or turning large or heavy loads or are difficult to pack in tight spaces. With Cardinal, packages are sorted faster during the shipping procedure, reducing transportation time in the facility. Cardinal is trying to improve the efficiency of Amazon’s shipping operations by converting batch-based manual work into computerized work.

Kermit

Kermit is an Autonomously Guided Cart (AGC) that moves empty totes from one location to another throughout the facility and allows staff to return the empty totes to the starting line. Kermit navigates by following strategically placed magnetic tapes and tags along the way to decide whether to speed up or slow down. Kermit is still under development and testing in several parts of the United States.

References

1 - Big Data Analytics: The Main Driver of the Fourth Industrial Revolution

2- E2EE

Lutkevich, Ben, and Madelyn Bacon. “What Is End-to-End Encryption (E2EE) and How Does It Work?” SearchSecurity, TechTarget, 25 June 2021, www.techtarget.com/searchsecurity/definition/end-to-end-encryption-E2EE.

Pound, Mike. “End to End Encryption (E2EE) - Computerphile.” YouTube, 30 Mar. 2017, www.youtube.com/ watch?v=jkV1KEJGKRA.

3- Arabic Natural Language Processing: Problems and Potential

IBM Cloud Education. (2 July 2020). Natural Language Processing (NLP). https://www.ibm.com/cloud/learn/natural-language-processing

What is Natural Language Processing? (n.d.). https://www.oracle.com/artificial-intelligence/what-is-natural-language-processing/ Larabi Marie-Sainte, S., Alalyani, N., Alotaibi, S., Ghouzali, S., & Abunadi, I. (2019). Arabic Natural Lan guage Processing and Machine Learning-Based Systems. IEEE Access, 7, 7011–7020. https://doi.org/10.1109/ access.2018.2890076

WTI. (2020, Dec 1). A Short Introduction to Arabic Natural Language Processing by Dr. Nizar Habash (NYU Abu Dhabi) [Video]. Youtube. https://www.youtube.com/watch?v=HXMKhPoLXqo

4- One Galaxy, An Entire Universe

Wood, C. (2022, Jan 20). Any Single Galaxy Reveals the Composition of an Entire Universe. Quanta Maga zine.

5- Are The Movies Tricking Us Or Is it Our Brain?

Jeffrey M. Zacks,(2015).Flicker: Your Brain on Movies book.Oxford University Press. Amanda Mei. ( 2015). What happens in the brain when we watch a movie?.yalescientific.org Cullen Traynor. ( 2019).Caring For Fictional Characters: & The Neuroscience Behind It.writingcooperative. com

Annie Hudson . (2020)Emotional attachments to fictional characters.nvnews.com.au

1) McCafferty, K. (2022, March 28). Dev-0537 criminal actor targeting organizations for data exfiltration and destruction. Microsoft Security Blog. Retrieved September 27, 2022, from https://www.microsoft. com/security/blog/2022/03/22/dev-0537-criminal-actor-targeting-organizations-for-data-exfiltration-anddestruction/

2)Internet crime complaint center (IC3): Criminals increasing sim swap schemes to steal millions of dollars from US public. Internet Crime Complaint Center (IC3) | Criminals Increasing SIM Swap Schemes to Steal Millions of Dollars from US Public. (n.d.). Retrieved September 27, 2022, from https://www.ic3.gov/Media/ Y2022/PSA220208

3)Wikimedia Foundation. (2022, September 26). Lapsus$. Wikipedia. Retrieved September 27, 2022, from https://en.wikipedia.org/wiki/Lapsus$

4)Zwiezen, Z. (2022, September 23). Report: 17-year-old arrested on suspicion of being hacker behind GTA VI leak. Kotaku. Retrieved September 27, 2022, from https://kotaku.com/gta-6-vi-hack-leaker-arrestedteenager-london-laspsus-1849573250

5)RIFT: Research and Intelligence Fusion Team. (2022, April 28). LAPSUS$: Recent techniques, tactics and procedures. NCC Group Research. Retrieved September 27, 2022, from https://research.nccgroup. com/2022/04/28/lapsus-recent-techniques-tactics-and-procedures/

6)Nunnikhoven, M. (2022, April 5). What we can learn from lapsus$ techniques. Dark Reading. Retrieved September 27, 2022, from https://www.darkreading.com/vulnerabilities-threats/what-we-can-learn-fromlapsus-techniques

7- Do phones hear our conversations? Fact or Myth?

The Washington Post. (2021, November 12). Ask Help Desk: No, your phone isn’t listening to your conversations. Siriously. Website. https://www.washingtonpost.com/technology/2021/11/12/phone-audio-targeting-privacy/

SPIRALYTICS. (2020, August 20). Can your Phone Hear Your Conversation? (Yes, But Here’s How). Website. https://www.spiralytics.com/blog/mobile-ads-can-phone-hear-conversations-infographic/

The Converstaion. (2021, June 20). Is your phone really listening to your conversations? Well, turns out it doesn’t have to. Website. https://theconversation.com/is-your-phone-really-listening-to-your-conversationswell-turns-out-it-doesnt-have-to-162172

Cubeware GmbH. (2020, October 23). Is your phone listening to you? [Video]. YouTube. https://youtu.be/ u2Q8DqXch7w

8- Can Computers see?

Bython Media (2021, Jan 26). What is Computer Vision? [Video]. Website: https://www.youtube.com/watch?v=LTlZy-OVWkM

Accenture (2019, Feb 2019). AI 101: What is Computer Vision? | Accenture [Video].

IBM. (n.d.). What is computer vision?.https://www.ibm.com/jo-en. Retrieved September, 27, 2022,from https://www.ibm.com/topics/computer-vision

v7labs. (August, 1, 2022). 27+ Most Popular Computer Vision Applications in 2022 Website. https:// www.v7labs.com/blog/computer-vision-applications

9- Countess Lovelace

Johnson, Connor. “Famous Computer Scientists Throughout History | GCU Blog.” Grand Canyon University, 25 January 2022, https://www.gcu.edu/blog/engineering-technology/famous-computer-scientiststhroughout-history

The Editors of Encyclopaedia Britannica, and Erik Gregersen. “Ada Lovelace | Biography, Computer, & Facts | Britannica.” Encyclopaedia Britannica, 26 August 2022, https://www.britannica.com/biography/Ada-Lovelace. Accessed 27 September 2022.

Johnson, Lily. “10 Facts About Ada Lovelace: The First Computer Programmer.”History Hit, 13 April 2021, https://www.historyhit.com/facts-about-ada-lovelace-the-first-computerprogrammer/. Accessed 27 September 2022.

Fearn, Alison. “Inspirational quotes: Ada Lovelace | The first computer programmer.” WeAreTechWomen, 10 December 2021, https://wearetechwomen.com/inspirationalquotes-ada-lovelace-the-first-computer-programmer/. Accessed 27 September 2022.

10- Top 10 mistakes of entrepreneurs

UCBerkeleyHaas. “Guy Kawasaki: The Top 10 Mistakes of Entrepreneurs.” YouTube, YouTube, 11 Mar. 2013, https://www.youtube.com/watch?v=HHjgK6p4nrw.

“How Many Cats Are There in the UK?” Cats Protection, https://www.cats.org.uk/catsblog/how-many-cats-in-the-uk.

11- Amazon supply chain

Amazon. (2022). Amazon Robotics Uses Amazon SageMaker and AWS Inferentia to Enable ML inferencing at Scale. Website.https://aws.amazon.com/solutions/case-studies/amazon-robotics-casestudy/

Chang, B. (2021). Meet Amazon’s new robots designed to reduce warehouse injuries. Website. https://www.businessinsider.com/photos-amazon-robots-warehouse-robots-reduce-warehouseinjuries-2021-6

Dugan, K. (2021). Meet ‘Bert’ and ‘Ernie,’ Amazon’s newest warehouse robots. Website. https://www.wftv.com/news/trending/meet-bert-ernie-amazons-newest-warehouse-robots/ M5CY7BX7EJDZ3AOQOE7LEINL5U/

Staff, M. (2021). Analyst study: Revenues from warehouse robotics to top $51 billion by 2030. Website. https://www.scmr.com/article/analyst_study_revenues_from_warehouse_robotics_to_top_51_billion_ by_2030

Images References

All the images were taken from the following websites:

https://www.freepik.com https://www.unsplash.com https://burst.shopify.com/ https://pixabay.com https://istockphoto.com

Note that every image used in this issue doesn’t have any license and they are copyrighted but as long as the usage of the images complies with the usage rules we can use them.