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✨ Time Management — Lecture Summary

How to set goals?

  • Ask your self:
  • why do I want to do it? What is the goal?
  • Why will I succeed?
  • What happens if I choose not to do it?
  • If there was something on your to-do list, why is it there?

The 80-20 rule: The minimal number of things in your life or on your to-do list are going to contribute the vast majority of the value.

How to avoid wasting time?

“Being successful doesn’t make you manage your time well.”
“Managing your time well makes you successful.”

How to deal with a boss?

  1. Ask them: When is our next meeting?
  2. Ask them: What do you want me to have done by then?
  3. Ask them: Who can I turn to for help, besides you?

How to delegate to people?

  • “Do dirtiest and hardiest things by yourself”
  • “Trust your employees and give them the resources and support needed.”
  • “Be kind towards your people”
  • “Give a penalty/reward for them if they made a bad/good deed”
  • “Make your meetings phoneless”
  • “Make a one-minute summary of the task of everyone in the meeting.”

Skills and tools recommended to get more out of your day:

  • Buy a personal digital assistant
  • “Put your to-do list in priority order.”
  • “Do time journal, count your screen time “
  • “revisit this talk after 30 days and ask what did I change?”

How to deal with stress and procrastination?

  • “Make fake deadlines and act as if it is real”
  • Covey’s four-quadrant TODO
    1. Do the important in Due Soon
    2. Do the important in Not Due Soon
    3. Do the not important in Due Soon
    4. Do the not important in Not Due Soon
  • “A good file system is essential.”
  • “Keep your desk clean and clear.”
  • “Manage your time just like managing your money. “
  • “Make your office comfortable for you and, optionally, comfortable for others.”
  • “Thank you notes are very important; it is crucial to tell someone how much you appreciate things.”
  • “If things aren’t going well, that probably means you’re learning a lot, and it’ll go better later.”
  • “Falling to plan is planning to fail.”
  • “Plan Each Day, Each Week, Each Semester”
  • “You can always change your plan, but only once you have one!”
  • “If you have got a bunch of things to do, do the ugliest thing first.”
  • “More monitors, more productivity”
  • “Don’t be rude to your office visitors.”

Inspiration:

“If you can dream it, you can do it.”- Disney
“Time is all we have, and you may find one day you have less than you think.” – Randy Pausch

Time journals.

Monitor yourself in 15-minute increments for between 3 to 14 days. Update every 30 minutes, not at the end of the day.

🔒 Cybersecurity Incidents in Qatar

Qatar National Bank (QNB) Data Breach – April 2016

What did attackers break into?
Hackers successfully infiltrated the internal databases of Qatar National Bank (QNB), the largest financial institution in Qatar, which has extensive international ties. The attack targeted core banking systems and customer records—systems responsible for managing user credentials, credit card information, and sensitive high-profile client profiles.

How did attackers break into it?
Although QNB did not provide an official technical breakdown of the breach, cyber analysts suspect that the attack relied on phishing for credential harvesting and the exploitation of insider access. The stolen data was not only accessed but was also leaked on the internet, totaling 1.4 GB. The leaked files included spreadsheets containing usernames, passwords, credit card numbers, phone numbers, and classified intelligence and diplomatic profiles, including those of MI6 and French DGSE agents, as well as journalists (Tabassum & Mustafa, 2018). An analysis indicated that this breach was possibly driven by espionage motives, given the sensitive nature of the leaked intelligence data.

Who was impacted?
The breach affected thousands of customers, including:

  • Government officials
  • Foreign diplomats
  • Journalists
  • Military and intelligence officers
  • Private citizens with QNB accounts

The leak revealed detailed metadata, suggesting significant surveillance on specific targets. In essence, this breach did not merely reflect a commercial failure; some viewed it as a national security threat.

How was it investigated?
The breach prompted an international response. Qatar’s Ministry of Interior, national cybersecurity teams, and foreign partners initiated a detailed digital forensic investigation. QNB never publicly acknowledged the full extent of the breach but emphasized the steps taken to protect customers and enhance security. The incident highlighted the vulnerabilities of digital banking systems against hybrid threats that blend espionage, politics, and finance. As a result, the breach catalyzed regulatory reforms, including stricter compliance standards and the establishment of dedicated national cybersecurity teams (Brown, 2018).

RasGas Malware Attack – August 2012

What did attackers break into?
RasGas, one of the world’s leading liquefied natural gas (LNG) producers headquartered in Qatar, became the target of a significant cyberattack that paralyzed its IT infrastructure. This included internal communications, email servers, and document-sharing systems. The incident mirrored the well-known Shamoon virus attack on Saudi Aramco just weeks earlier, suggesting coordinated cyber warfare against Gulf energy infrastructure.

How did attackers break into it?
The attackers deployed Shamoon, a type of wiper malware that:

  • Overwrites the master boot record (MBR)
  • Deletes files
  • Renders devices completely unusable

Once the malware infiltrated RasGas’s systems—likely through phishing emails or infected USB drives—it spread laterally through networked computers. The systems lacked endpoint detection or segmentation, allowing Shamoon to replicate quickly and cause widespread failure. Unlike traditional ransomware, Shamoon was purely destructive and had no ransom demand; its purpose appeared to be geopolitical sabotage rather than profit (Alomari et al., 2025).

Who was impacted?
The attack disrupted RasGas's internal systems for several days, resulting in:

  • Email, communication, and procurement systems are going offline
  • Disruption in coordination with partners for shipping and distribution
  • Although production facilities were isolated from IT networks, the supply chain faced setbacks

The incident alarmed both Qatar and its energy-importing partners in Asia and Europe, revealing that cyberattacks could threaten national energy exports and global energy security.

How was it investigated?
The attack was treated as a national emergency. Investigations included:

  • International cybersecurity consultants
  • The National Cybersecurity Agency (NCSA)
  • Coordination with Western intelligence agencies

This incident was among the first significant cyberattacks that prompted a Gulf country to modernize its Industrial Control Systems (ICS) protections. RasGas upgraded its network segmentation, malware detection capabilities, and incident response protocols. The attack also influenced the development of the region’s critical infrastructure protection policies (Al-Mhiqani et al., 2018).

Broader Impact on Qatari Cybersecurity Policy

  • The government introduced stricter cybercrime legislation and data protection laws.
  • The Qatar Computer Emergency Response Team (Q-CERT) was expanded and granted more authority.
  • National awareness campaigns and public-private collaboration initiatives were launched.
  • Investment in cyber education, particularly at the university level, has increased.

Conclusion

The 2012 RasGas malware incident underscored the critical need for enhanced cybersecurity measures in the region, prompting significant legislative and operational changes within Qatar's national cybersecurity framework.

📘 References

💻 Programming Languages — Q&A

1. Why did we move from punch cards to programming languages? What does that tell you about the purpose of programming languages?

In the early days of computing, programmers used punch cards to communicate directly with machines. This method was tedious, error-prone, and demanded an intimate understanding of hardware. The invention of programming languages provided a way to abstract machine operations into human-readable instructions, significantly improving efficiency and productivity. The transition highlights that the central purpose of programming languages is to make complex computing tasks more intuitive for humans, while still precise enough for machines to execute (Bergin & Gibson, 1996; Strachey, 2000).

2. There are hundreds of different programming languages out there. Why do you think we need so many?

The diversity of programming languages arises from the variety of problems they aim to solve. Some languages are optimized for scientific computing (e.g., Fortran, MATLAB), others for system-level performance (e.g., C, Rust), and still others for web applications (e.g., JavaScript, PHP). New languages often emerge to address limitations of older ones, balancing trade-offs in speed, usability, safety, and abstraction (Biddle, 1987; Landin, 1966).

3. What are some drawbacks of the programming language you use? How would you like it to be different?

For instance, Python is praised for its readability but criticized for slower execution speed compared to compiled languages, as well as its reliance on dynamic typing, which can cause runtime errors. A desirable improvement would be stronger static type support. In contrast, C offers high performance and low-level control but lacks memory safety, making it error-prone. Future designs could integrate automatic memory management while preserving efficiency (Hudak, 1989; Ben-Ari, 1996).

4. If you were going to create a new programming language, how would you start? What do you need to define?

Designing a language begins with identifying its purpose and domain. For example, will it be general-purpose or specialized (e.g., data science, embedded systems)? Next, one must define its syntax and semantics, establish a type system (static vs dynamic), and choose its execution model (compiled, interpreted, or hybrid). Equally important is building an ecosystem—libraries, compilers, and debugging tools—to support adoption. Successful languages evolve iteratively: creators first solve real-world problems, refine syntax, and improve features over time (Pierce, 2002; Jepsen, 1999).

📘 References

Decision Problems and the P vs NP

1. What is a decision problem?

A decision problem is a type of computational problem where the output is restricted to two answers: yes or no. For example, the questions “Is 17 a prime number?” or “Does this graph contain a cycle?” are both decision problems. The study of such problems is central to computational complexity theory because it allows classification of problems by their solvability and verifiability (Goldreich, 2010).

2. What does it mean for a decision problem to be decidable?

A problem is called decidable if there exists an algorithm that always gives a correct answer (“yes” or “no”) for every input within a finite amount of time. In other words, it can be solved by a Turing machine that always halts. For example, determining whether a string belongs to a given regular expression is decidable. In contrast, some problems are undecidable, such as the Halting Problem (Cook, 2000).

3. What is the class P? What is the class NP?

The class P contains all decision problems that can be solved in polynomial time by a deterministic algorithm.

The class NP consists of decision problems for which proposed solutions can be verified in polynomial time, even if finding the solutions might be much harder.

Every problem in P is also in NP, but it remains unknown whether P = NP (Stockmeyer, 1987; Wigderson, 2006).

4. What is the intuitive meaning of the “P versus NP” question?

The P vs. NP problem asks: Can every problem whose solution can be quickly verified (NP) also be quickly solved (P)? Put simply: does “easy to check” imply “easy to solve”? This remains one of the greatest unsolved problems in computer science. Most researchers believe that P ≠ NP (Schaefer, 1978).

5. What happens if the P vs. NP problem is solved?

Resolving the P vs. NP problem would earn the solver a $1,000,000 prize from the Clay Mathematics Institute. More importantly, it would transform fields such as cryptography, artificial intelligence, and applied mathematics (Dean, 2015).

References

Cloud Computing: Concepts, Models, and Applications

1. What is cloud computing?

Cloud computing is an IT deployment model where computing resources, such as servers, storage, and applications, are delivered over the internet on demand. It uses virtualization to provide scalability and flexibility, allowing users to pay only for what they use (Böhm et al., 2011, [Link]).

2. Is cloud computing a new technology?

Cloud computing is not entirely new; it is an evolution of earlier models like grid and utility computing. Its uniqueness comes from on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service, which together define its distinct features (Gorelik, 2013, [Link]).

3. What are the three major cloud service models?

The three main service models are:

  • Infrastructure as a Service (IaaS): virtualized hardware resources.
  • Platform as a Service (PaaS): developer environments for building apps.
  • Software as a Service (SaaS): end-user applications delivered via the cloud (Gibson et al., 2012, [Link]).

4. Real-world domains of application:

Cloud computing is widely used in:

  • Healthcare for managing large medical datasets.
  • Finance for secure, scalable transaction processing.
  • Education for e-learning platforms and collaborative tools (Weinhardt et al., 2009, [Link]).

5. What is the business model of cloud computing?

The economic model of cloud computing is based on pay-as-you-go and subscription pricing. This approach enables cost efficiency and scalability. It also supports new business processes by reducing initial IT investment and enabling new service offerings (Chang et al., 2010, [Link]; Berman et al., 2012, [Link]).

Artificial Intelligence (AI) — Q&A

1. How would you define Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the science of creating systems capable of performing tasks that usually require human intelligence, such as perception, reasoning, learning, and decision-making (Saghiri et al., 2022, [Link]).

2. Why is AI experiencing impressive growth right now?

Although AI has existed since the 1950s, its current growth is fueled by three drivers: (1) massive availability of big data, (2) improved computational resources like GPUs and cloud infrastructure, and (3) breakthroughs in machine learning and deep learning (Perez et al., 2018, [Link]).

3. What are three domains where AI still faces challenges?

AI models still struggle in:

  • Commonsense reasoning and general knowledge.
  • Transparency/explainability of complex models.
  • Robustness and safety in unpredictable environments (Yenduri et al., 2025, [Link]).

4. What are three application sectors where robots are widely used, and why?

Robots are common in:

  • Manufacturing for precision and automation.
  • Healthcare for surgeries and elderly assistance.
  • Logistics for warehouse and delivery tasks.

They are used to improve efficiency, reduce costs, and ensure safety (Bogue, 2014, [Link]).

5. Is AGI + autonomous robotics imminent and impactful?

The convergence of Artificial General Intelligence (AGI) and fully autonomous robotics is not imminent. However, if achieved, it could transform industries, economies, and daily life with profound global impact (Sowmya et al., 2025, [Link]).

References

Computer Vision — Phone Applications & Observations

1. Name three applications of Computer Vision you can recognize on your phone

  • Snapchat — applies AR filters to user images.
  • Face ID — uses a biometric faceprint to unlock the phone and authorize actions such as Apple Pay.
  • Measure — estimates measurements and dimensions of objects.

2. Take a photo of your desk and ask ChatGPT to identify or name all the objects

How many steps are involved in this identification process?
7

Can you identify any challenges for ChatGPT?
Yes. ChatGPT struggled to identify distant items and objects that were not clearly visible. For example, a calculator placed on my mousepad was misclassified as a mouse because the calculator did not fully appear in the image.

Ask ChatGPT to highlight or circle a specific item on your desk. Describe the results and possible challenge.
ChatGPT still thought the calculator was a mouse, and in the generated image it placed a mouse instead of the calculator. It also failed to highlight items that were close to each other: it saw my thope (a white men's dress) and highlighted it as the table, likely because both were white and appeared very close due to the camera angle.

3. Several applications involve decision-making processes that rely on Computer Vision. Name some applications that you believe are not trustworthy or safe.

  1. Facial recognition for law enforcement
    Why unsafe: Many facial recognition systems exhibit racial and gender biases, leading to false identifications and wrongful arrests.
    Example issue: Studies (e.g., MIT Media Lab) show higher misidentification rates for people with darker skin tones.
    Risk: Unjust punishment, invasion of privacy, and surveillance abuse.
  2. Self-driving cars (in edge or unpredictable cases)
    Why untrustworthy: Vision models can misinterpret weather, shadows, or unusual road situations.
    Risk: Accidents or fatalities if the system fails to detect a pedestrian, traffic light, or road sign correctly.
  3. Automated medical diagnosis apps
    Why risky: Apps that diagnose diseases from photos (e.g., skin cancer, X-rays) often lack proper clinical validation.
    Risk: False positives/negatives may lead to incorrect treatment or delayed medical care.

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